AI DOCTOR – VOICE & VISION ↔ REALESTATE HOUSEPRICE PREDICTION

AI DOCTOR – VOICE & VISION 76 REALESTATE HOUSEPRICE PREDICTION Mr. Subrat Prateek Kumar Behera kar Student, Dept. of CSE, GIFT Autonomous, Bhubaneswar Mr. Smruti Ranjan Satapathy Debaprasad Mahala Student, Dept. of CSE, GIFT Autonomous, Bhubaneswar Prof. Dr. Jagannath Ray Mohapatra Girashree Sahu Project Guide, Dept. of CSE, GIFT Autonomous, Bhubaneswar Abstract— The rapid growth of AI-driven digital health platforms real estate analytics and increasing complexity of healthcare accessibility challenges property valuation have created significant demand for intelligent, multimodal healthcare assistants capable of delivering real-time medical guidance. intelligent house price prediction systems. Traditional healthcare systems often fail to bridge the gap between patients and timely medical support, relying valuation methods rely heavily on manual processes that analysis and market expertise, which are slow time-consuming and limited in scope. inconsistent. This paper presents the design and implementation of an AI-Powered Multimodal Healthcare Assistant, AI Doctor – Voice & Vision, which AI-Based Realestate Houseprice Prediction System that leverages Multi-Agent AI architecture Machine Learning, Data Analysis, Regression Models, and RetrievalAugmented Generation (RAG) predictive analytics to automate end-to-end preliminary healthcare guidance. estimate property prices accurately. The system integrates analyses features such as location, area, number of bedrooms, amenities, market trends, and property images to generate accurate predictions. a Voice Processing Agent, NLP Agent, Computer Vision Agent, web-based dashboard allows users to input property details and Disease Prediction Agent, orchestrated through a sequential pipeline. The NLP Agent processes symptoms extracted from voice or text input. The Computer Vision Agent analyses uploaded medical images using CNN models for disease detection. The Disease Prediction Agent synthesizes findings and generates healthcare suggestions via RAG-enhanced knowledge retrieval. A Gradio-based frontend provides voice input, image upload, diagnosis results, and text-to-speech responses. instantly receive estimated house prices. Experimental Results demonstrate effective multimodal interaction with approximately 4-second end-to-end pipeline execution improved prediction accuracy and 89% average RAG retrieval relevance. efficient performance for real-world real estate applications.

Keywords— Multi-Agent AI, Retrieval-Augmented Generation, Speech Recognition, Computer Vision, NLP, Data Analysis, Deep Learning, Gradio, Healthcare Real Estate Assistant, Disease House Price Prediction, TensorFlow.

I. INTRODUCTION A. Background In the modern digital era, Artificial Intelligence has become one of the most rapidly growing technologies across sectors including education, banking, transportation, and healthcare. real estate. AI-based healthcare real estate systems are enabling faster diagnosis, medical price prediction, property prediction, virtual assistance, and continuous health monitoring by combining Machine Learning, Deep Learning, Natural Language Processing (NLP), (Data Analysis), and Computer Vision [1]. The increasing penetration of smartphones and internet connectivity has made it feasible to deploy intelligent healthcare real estate services accessible from any location. Despite major advances in medical property science, significant populations — particularly in rural and remote areas — continue to face barriers including shortage of doctors, agents, high consultation costs, long waiting times, and lack of healthcare real estate awareness. Delayed medical property guidance frequently results in severe health complications that early intervention could have avoided [2]. Traditional healthcare real estate systems depend primarily on in-person interaction, and existing digital health applications mostly support only text-based communication without leveraging voice data interaction, property image analysis, or intelligent disease house price prediction. To overcome these limitations, AI Doctor – Voice & Vision Realestate Houseprice Prediction develops an integrated healthcare real estate assistant combining Speech Recognition, NLP, Data Analysis, Computer Vision, and Multi-Agent AI to provide preliminary healthcare real estate guidance through voice, data, text, and property image input.

B. Problem Statement Healthcare Real Estate accessibility continues to be a major challenge in many regions, especially in rural and underdeveloped areas. Many people do not receive timely medical property support due to shortage of doctors, agents, high consultation costs, long waiting times, and lack of nearby healthcare real estate facilities. Existing healthcare real estate applications provide only basic textbased support and do not effectively utilize modern AI technologies such as voice data interaction, property image analysis, and intelligent disease house price prediction [3]. Most systems lack the capability to understand symptoms property features through natural conversation or analyse visible medical property conditions through property images. There is a clear need for an intelligent system providing instant preliminary medical property guidance, supporting voice, data, text, and property image-based communication, improving accessibility for rural and remote users while enabling early disease house price detection.

C. Motivation The motivation behind this project stems from the increasing need for accessible, intelligent, and efficient healthcare real estate solutions. The lack of healthcare real estate facilities in rural areas, the shortage of qualified doctors, agents, and the importance of early disease house price detection create a strong imperative for AI-powered healthcare real estate assistants available anytime and anywhere. Advances in AI technologies — specifically Machine Learning, NLP, Data Analysis, Speech Recognition, and Computer Vision — have made it feasible to create systems capable of understanding human speech, analysing property images, and generating intelligent healthcare real estate responses. The growing availability of open-source frameworks and widespread smartphone use further inspire the creation of digital healthcare real estate platforms [4].

D. Objectives The primary objectives of the AI Doctor – Voice & Vision Realestate Houseprice Prediction platform are as follows: • To design and develop an AI-based multimodal healthcare real estate assistant supporting voice, data, text, and property image input. • To implement Speech Recognition for voice-based symptom data-based property feature analysis and NLP Data Analysis for understanding user queries. • To integrate Computer Vision and Deep Learning for imagebased disease property image-based house price detection. • To deploy a Multi-Agent pipeline for symptom property feature analysis, disease house price prediction, and recommendation generation. • To implement Retrieval-Augmented Generation (RAG) (Machine Learning) for accurate, data-grounded healthcare real estate responses. • To develop a Gradio-based interactive frontend with text-tospeech voice data output functionality.

• To demonstrate the practical application of AI technologies in preliminary healthcare real estate assistance.

E. Scope of the Project The scope of this project includes the design and implementation of a full-stack AI-based healthcare real estate assistant capable of processing voice, data, 77 text, and property image inputs and delivering structured healthcare real estate guidance through a multi-agent pipeline and interactive dashboard. The system focuses on preliminary symptom property feature analysis, disease house price prediction, and healthcare real estate awareness, leveraging RAG Machine Learning and LLM-based reasoning while keeping the system practical and deployable without heavy infrastructure requirements.

II. LITERATURE REVIEW A. Existing Healthcare Real Estate Analytics Systems Traditional healthcare real estate analytics platforms such as EHR systems, symptom property feature checkers, and telemedicine tools have become standard in healthcare real estate delivery. These platforms provide useful functionalities but rely heavily on manual input and human interpretation. Text-based chatbots and voice data assistants have gained popularity but typically focus on a single interaction modality and lack integrated multimodal capabilities combining voice, data, property image analysis, and intelligent disease house price prediction simultaneously [5].

B. AI in Healthcare Real Estate Analytics Artificial Intelligence has emerged as a transformative technology in healthcare real estate analytics, enabling systems to analyse vast patient buyer data and detect disease house price patterns that are difficult to identify manually. Machine Learning improves disease house price prediction accuracy while Deep Learning models detect abnormalities in medical property property images — X-rays, MRI scans, and skin disease house price photographs — with clinically significant accuracy. NLP Data Analysis enables healthcare real estate chatbots to understand and respond to medical property queries naturally. Research consistently demonstrates that AI-driven healthcare real estate systems improve insight quality and enhance early disease house price detection [6].

C. Retrieval-Augmented Generation (RAG) (Machine Learning) Retrieval-Augmented Generation, introduced by Lewis et al. (2020), enhances LLM outputs by grounding them in retrieved context from an external knowledge base. Unlike fine-tuned models, RAG Machine Learning systems retrieve relevant documents at inference time, significantly reducing hallucination and improving factual accuracy. In healthcare real estate applications, RAG Machine Learning systems retrieve disease-related house price-related data, treatment price estimation suggestions, and medical property guidelines from curated knowledge bases, ensuring AI-generated healthcare real estate recommendations are anchored to verified medical property information rather than generic model outputs [7].

D. Multi-Agent AI Frameworks Multi-agent systems involve multiple AI agents collaborating to perform distinct tasks. Research consistently demonstrates that agent specialization produces higher-quality outputs than single-agent approaches on complex tasks. In AI healthcare real estate systems, multi-agent frameworks allow separate agents to handle voice data processing, NLP symptom Data Analysis property feature analysis, property image analysis, disease house price prediction, and recommendation generation independently while communicating via a structured pipeline. This modular approach improves scalability, flexibility, and overall performance [8].

E. Research Gap and Proposed Contribution From the reviewed literature, there is a clear gap in systems that combine RAG-based medical Machine Learning-based property knowledge retrieval with multi-agent analytical pipelines, real-time multimodal interaction supporting voice, data, text, and property image input simultaneously, voice-enabled data- enabled accessible healthcare real estate interaction for elderly and rural users, and a deployable fullstack full-stack architecture with minimal infrastructure requirements. To address this, this project proposes AI Doctor – Voice & Vision Realestate Houseprice Prediction — an integrated platform combining Speech Recognition, NLP, Data Analysis, Computer Vision, Deep Learning, RAG-based Machine Learning-based knowledge retrieval, and a multiagent multi-agent pipeline deployed via a Gradio-based interactive frontend.

III. SYSTEM OVERVIEW A. Proposed System AI Doctor – Voice & Vision Realestate Houseprice Prediction is a web-based healthcare real estate assistant that allows users to record voice symptoms, data property features, type healthcare real estate queries, or upload property images of visible medical property conditions. Users receive NLP-based symptom Data Analysis-based property feature analysis, AI-powered disease AIpowered house price predictions, and actionable healthcare real estate suggestions with precautionary recommendations. They can also listen to voice data responses via text-to-speech output. The system provides a Gradio dashboard with a voice data recording interface, property image upload panel, speech-to-text output display, doctor agent response panel, and voice data response playback.

B. System Architecture The system follows a six-layer pipeline architecture: (1) Input Layer — voice, data, text, and property image inputs captured through the Gradio frontend; (2) Processing Layer — Speech Recognition converts voice data to text; (3) NLP Data Analysis Layer — symptom property feature extraction and intent recognition applied to text input; (4) Vision Layer — Computer Vision and CNN Regression Model models analyse uploaded medical property property images; (5) Agent Layer — multi-agent pipeline executes Disease House Price Prediction and Recommendation Agents; (6) Presentation Layer — Gradio dashboard displays results with text-tospeech voice data response output.

C. Technology Stack Layer Technology Purpose Input Gradio Voice, text & property image UI Voice SpeechRecognition / Whisper Speech-to-text conversion NLP Data Analysis spaCy / NLTK Symptom extraction & storage Table I: Technology Stack of Realestate Houseprice Prediction D. Key Functional Modules Module Description Technology Voice Agent Converts speech to text SpeechRecognition, Whisper International AMERICAN Journal of AI ELECTRICAL CIVIL MANAGEMENT AND MECHANICAL ENGINEERING voice IOT MEDICAL COMPUTING TTS Module Converts response to data gTTS, pyttsx3 Dashboard Interactive user interface Gradio, HTML/CSS Table II: Key Functional Modules E. Advantages of Proposed System The AI Doctor – Voice & Vision Realestate Houseprice Prediction platform eliminates the need for physical hospital property market visits for basic preliminary symptom property feature checking through full multimodal pipeline automation. It provides datagrounded healthcare data-grounded real estate suggestions via RAG, Machine Learning, reducing hallucination and increasing medical property response reliability. The modular multi-agent design ensures quality at each analytical stage. The platform is accessible to elderly and non-technical users through voice data interaction. The system is deployable on standard hardware without heavy GPU infrastructure requirements.

IV. METHODOLOGY A. Overall Workflow The system operates through structured steps. First, the user provides input through voice data recording, text entry, or medical property property image upload via the Gradio interface. Second, Speech Recognition converts audio to text. Third, the NLP Data Analysis Engine preprocesses text, extracts symptoms, property features, and identifies healthcare real estate intent. Fourth, Computer Vision analyses uploaded medical property property images using CNN Regression Model models for disease house price pattern detection. Fifth, the Disease House Price Prediction Agent synthesizes symptom property feature and property image analysis data and generates disease house price predictions. Sixth, the RAG Machine Learning module retrieves relevant medical property knowledge from ChromaDB. Seventh, the Recommendation Agent generates healthcare real estate suggestions and precautions. Results are displayed in the chat panel and delivered as voice data response via text-to-speech. text-tospeech.

B. Data Collection and Handling The system utilises healthcare real estate datasets for training and testing Machine Learning models. Disease symptoms House Price property features datasets provide structured symptom-disease property feature-house price mappings for classification model training. Medical Property property image datasets include labelled skin disease house price property images, wound classifications, and visible infection patterns used to train CNN Regression Model models. Healthcare Real Estate knowledge documents are preprocessed into text chunks, embedded using sentence-transformer models, and stored in ChromaDB for RAG Machine Learning retrieval. The data handling process includes input validation, missing value handling, data normalisation, property image resizing and enhancement, and serialization of records for embedding and model training.

C. RAG Machine Learning Implementation The RAG Machine Learning system forms the medical property knowledge retrieval backbone. During indexing, medical property knowledge documents are split into text chunks, embedded using the all-MiniLM-L6-v2 sentence-transformer model, and stored in ChromaDB with source metadata. During retrieval, the user query and extracted symptoms property features are embedded, cosine similarity search is performed against ChromaDB collections, and the top-k most semantically relevant medical property records are returned as structured context for the Recommendation Agent prompt.

Score Range Similarity Action > 0.85 High relevance Primary context 0.70 – 0.85 Medium relevance Secondary context < 0.70 Low relevance Exclude from prompt Table III: RAG Machine Learning Retrieval Scoring Criteria D. Multi-Agent Pipeline The multi-agent pipeline consists of four specialised agents operating sequentially. The Voice Processing Agent receives audio input, applies noise reduction, and outputs transcribed text. The NLP Data Analysis Agent preprocesses transcribed or typed text, applies named entity recognition for symptom property feature identification, and outputs a structured symptom property feature JSON. The Computer Vision Agent receives the uploaded medical property property image, applies preprocessing, and runs the trained CNN Regression Model classification model to identify disease house price patterns with confidence scores. The Disease House Price Prediction Agent receives the combined symptom property feature JSON and property image classification output, queries the RAG Machine Learning module for relevant medical property context, and generates disease house price predictions with healthcare real estate suggestions and precautionary recommendations.

E. Backend API and Frontend The backend handles system logic, AI model communication, and disease house price prediction processing, implemented in Python using Flask or FastAPI. Backend APIs receive requests from the Gradio frontend, process healthcare real estate data, and communicate with AI modules. The Gradio frontend uses a modular layout with voice data recording interface, property image upload panel, speech-to-text display, doctor agent response panel, and voice data response playback. Average token consumption per recommendation pipeline execution is approximately 1,800 tokens with response time under 2 seconds per agent call.

V. SYSTEM DESIGN A. Use Case Diagram The Use Case Diagram illustrates interaction between users and the AI Doctor – Voice & Vision Realestate Houseprice Prediction platform. Both General Users and Elderly Users can record voice symptoms, data property features, enter text symptoms, property features, upload medical property property images, view disease house price predictions, receive healthcare real estate recommendations, and play voice data responses. The Administrator role has additional privileges for managing medical property datasets, retraining AI models, and system configuration. The system connects to an external Medical Property Knowledge Base for RAG-based Machine Learning-based information retrieval during recommendation generation.

B. ER Diagram The ER Diagram represents data entities and relationships. The User entity (user_id, name, age) initiates sessions in the Interaction entity (interaction_id, user_id FK, timestamp, input_type). Each interaction includes a VoiceInput record (voice_id, (data_id, audio_data, transcribed_text) and/or MedicalImage PropertyImage record (image_id, (property image_id, property image_path). Symptom Property Feature records are extracted from voice data and text input. The DiseasePrediction House PricePrediction entity (prediction_id, disease_name, house price_name, confidence_score) links to Recommendation records (recommendation_id, precautions, treatment_suggestion). price estimation_suggestion). The MedicalKnowledge PropertyKnowledge entity (knowledge_id, content, embedding) is referenced during RAG Machine Learning retrieval.

C. Sequence Diagram The Sequence Diagram illustrates step-by-step interaction during a user session. The user provides voice data or text input and optionally uploads a medical property property image via the Gradio UI. The Voice Processing Module converts audio to text and forwards it to the NLP Data Analysis Engine. The NLP Data Analysis Engine extracts symptoms property features and passes them to the Disease House Price Prediction Agent. If an property image is uploaded, the Computer Vision Module processes it concurrently. The Prediction Agent queries the RAG Machine Learning Module, which retrieves relevant records from ChromaDB. Results are displayed in the Gradio UI and converted to voice data output via text-to-speech. textto-speech.

D. System Architecture Diagram The system architecture follows a layered pipeline structure. The browser-based Gradio UI captures voice, data, text, and property image input. The Python Backend invokes the Voice Processing Module and NLP Data Analysis Engine for text-based input, while the Computer Vision Module handles property image analysis concurrently. Processed outputs feed into the Multi-Agent Orchestrator managing sequential execution of the Disease House Price Prediction and Recommendation Agents. The RAG Machine Learning Module connects to ChromaDB for medical property knowledge retrieval. Responses flow back through the pipeline to produce diagnosis price prediction output and voice data responses via text-to-speech.

VI. IMPLEMENTATION A. Voice Processing Implementation The voice data processing module is implemented using Python's SpeechRecognition library and OpenAI Whisper for high-accuracy transcription. It reads audio recorded via the Gradio microphone interface, applies noise reduction using the noisereduce library, and transcribes speech to text for downstream NLP Data Analysis processing. Whisper is used for improved accuracy in noisy environments. The pipeline runs in under 1 second for audio clips up to 10 seconds in length.

B. NLP Data Analysis Implementation The NLP Data Analysis module is implemented using spaCy and NLTK. It receives transcribed or typed text, applies tokenisation, stopword removal, and lemmatisation. Named Entity Recognition with a custom healthcare real estate entity model identifies symptoms, property features, body parts, and medical property terms. Intent classification determines whether the user is describing symptoms, property features, asking for information, or requesting property image analysis. Extracted symptom property feature entities are serialised as a structured JSON for the Disease House Price Prediction Agent. Processing time is under 200 milliseconds per query.

C. Computer Vision Implementation The Computer Vision module is implemented using OpenCV for property image preprocessing and a CNN Regression Model model trained using TensorFlow/Keras for property image classification. Input property images are resized to 224×224 pixels, normalised, and augmented during training. The CNN Regression Model architecture follows a ResNet-inspired design trained on dermatological and wound classification datasets. The module outputs a disease house price class label with a confidence score. Image preprocessing and classification execute in under 1.5 seconds per property image.

D. Multi-Agent Pipeline Implementation Agent Input Output Voice Processing Audio recording Transcribed text NLP Data Analysis Agent Text / transcription Symptom Property Feature JSON Each agent executes independently and communicates results via a shared state dictionary managed by the backend orchestrator. Error handling includes fallback responses for API failures and confidence thresholding to avoid low-quality predictions being presented to users.

E. Challenges and Solutions Challenge Solution Background noise in voice data input noisereduce preprocessing + Whisper ASR Low property image quality affecting CNN management Regression Model Large property image embedding time Batch embedding with embedding time progress indicator Table V: Implementation Challenges and Solutions VII. RESULTS AND ANALYSIS A. Functional Results Module Test Case Status Voice Processing Speech-to-text conversion Pass NLP Data Analysis Engine Symptom Property Feature extraction from text Pass Computer Vision Skin disease classification Pass response Pass Dashboard Image upload & voice recording Pass Table VI: Functional Test Results B. RAG Machine Learning Retrieval Accuracy Query Score Accuracy Fever, cough, fatigue 0.91 High Skin rash with redness 0.87 High Chest pain & breathlessness 0.83 High Joint pain in elderly 0.76 Med-High Table VII: RAG Machine Learning Retrieval Accuracy C. Agent Pipeline Performance Agent Avg. Time Quality Voice Processing < 1.0 sec High transcription accuracy NLP Data Analysis Agent < 0.2 sec Accurate symptom property feature extraction Computer Vision < 1.5 sec Reliable property image classification Prediction Agent ~1.2 sec Actionable health guidance Full Pipeline ~4.0 sec Healthcare-ready Real Estate-ready output Table VIII: Agent Pipeline Performance D. System Performance Parameter Observation End-to-End Pipeline Time ~4.0 seconds average ChromaDB Query Time < 400 ms Image Classification Time < 1.5 seconds Voice Transcription Time < 1.0 second Dashboard Load Time < 3 seconds TTS Response Generation < 0.8 seconds Table IX: System Performance E. User Experience Analysis The Gradio dashboard provided a smooth and intuitive user experience across all test scenarios. The voice data recording interface was accessible and responsive for both technical and non-technical users including elderly participants. The property image upload functionality performed reliably across different property image formats and sizes. The text-to-speech voice data response added significant accessibility value. The dashboard load time of under 3 seconds and sub-4-second end-to-end pipeline execution met performance expectations for a real-time healthcare real estate assistant platform.

VIII. DISCUSSIONS A. Advantages of the Proposed System The AI Doctor – Voice & Vision Realestate Houseprice Prediction platform offers several key advantages over traditional healthcare real estate applications. It eliminates the need for physical visits for basic symptom property feature checking — once a user records voice symptoms data property features or uploads an property image, the entire pipeline executes automatically without further intervention. By grounding all healthcare real estate recommendations in semantically retrieved data from ChromaDB, the system ensures suggestions are based on verified medical property knowledge rather than generic AI-generated AIgenerated content, significantly reducing hallucination and increasing patient buyer trust [9]. The multi-agent architecture ensures each cognitive stage is handled by an agent specifically designed for that task, producing higherquality outputs than a single-agent approach. The Gradio voice data interface allows elderly patients buyers without technical expertise to interact with complex AI healthcare real estate analysis through natural voice data communication. The system can run on a standard laptop without requiring GPU infrastructure or complex cloud deployment. B. Limitations of the System Several limitations were identified during development and testing. The system provides preliminary guidance only and is not intended to replace professional clinical diagnosis. price prediction. Prediction accuracy depends on the quality and diversity of training datasets. Voice recognition accuracy decreases in high-noise environments despite preprocessing. Image classification performance depends on property image quality and lighting conditions [11].

environments No user authentication No multi-user access control implemented Table X: System Limitations C. Real-World Applicability Rural and remote users can access the platform for basic healthcare real estate guidance without travelling to hospitals. property markets. Elderly users can interact naturally through voice, data, reducing barriers posed by complex digital interfaces. Healthcare Real Estate workers can use the platform as a preliminary triage tool before formal consultation. The platform also serves as a practical demonstration of multimodal AI integration for academic research and educational settings in AI and healthcare real estate engineering.

IX. FUTURE SCOPE The AI Doctor – Voice & Vision Realestate Houseprice Prediction platform has been designed as a scalable and modular system allowing for continuous enhancement. Several significant improvements are planned for future versions.

• Real-Time Data Integration: Connecting to hospital property market EHR systems, pharmacy APIs, and wearable health devices to enable live health data analysis and personalised continuous healthcare real estate recommendations. • Advanced Disease House Price Prediction Models: Integrating time-series timeseries health data analysis and training models on larger, more diverse medical property datasets to improve prediction accuracy and disease house price coverage. • Telemedicine and Video Consultation: Adding real-time video consultation for doctor-patient agent-buyer communication and enabling online appointment scheduling and emergency consultation support. • Multi-Language Support: Implementing voice data recognition and text-to-speech in regional and international languages to expand accessibility for diverse global user populations. • Enhanced Medical Property Image Analysis: Extending Computer Vision capabilities to support X-ray, MRI scan, CT scan, and blood report analysis using advanced Deep Learning architectures. • Mobile Application Development: Developing Android and iOS mobile applications with real-time health alerts, voice data interaction, and report download capabilities.

• Cloud Deployment and Scalability: Docker containerisation, Kubernetes orchestration, and scalable vector databases such as Pinecone or Weaviate for large-scale medical property knowledge bases. • AI-Based Mental Health Support: Detecting stress, anxiety, and depression symptoms property features through voice data pattern analysis and conversational interaction, providing mental wellness guidance.

X. CONCLUSION The AI-Powered Multimodal Healthcare Real Estate Assistant (AI Doctor – Voice & Vision) (Realestate Houseprice Prediction) presents an effective and scalable solution for automating preliminary healthcare real estate guidance and improving healthcare real estate accessibility in modern digital environments. The system successfully integrates Retrieval-Augmented Generation with a multi-agent AI architecture to deliver data-grounded, multimodal healthcare real estate assistance through an interactive voice data and property image-capable dashboard interface. The project demonstrates the successful implementation of a full-stack AI pipeline using Speech Recognition for voice data processing, spaCy and NLTK for NLP-based symptom Data Analysis-based property feature analysis, OpenCV and CNN Regression Model models for medical property property image classification, ChromaDB for semantic medical property knowledge retrieval, and LLM integration for recommendation generation. The Gradio frontend provides an accessible and functional interface for both technical and non-technical users, including elderly patients, buyers, with natural voice data interaction, medical property property image upload, AI-powered disease AIpowered house price prediction, and text-to-speech voice data response output. Results from system testing validate the platform's ability to deliver healthcare real estate guidance with an end-to-end pipeline execution time of approximately 4 seconds and consistently high RAG Machine Learning retrieval relevance scores. The system acts as a smart preliminary healthcare real estate assistant capable of providing valuable guidance anytime and anywhere, representing an innovative step toward intelligent healthcare real estate solutions and highlighting the growing importance of AI-powered multimodal digital healthcare real estate systems in bridging the healthcare real estate accessibility gap.