This research proposes a comprehensive AI driven framework for real estate valuation by integrating Cognitive Digital Twins (CDTs), advanced predictive models, and blockchain technology to deliver a transparent, secure, and scalable solution. Leveraging machine learning and deep learning algorithms, the system predicts property prices based on key features including location, square footage, number of bedrooms, and market trends. Real-time data from external APIs ensures continuous updates in response to changing market dynamics. Blockchain serves as a decentralized, tamper-proof ledger for storing legal documents, transaction histories, and valuation records, thereby ensuring data integrity and trust across stakeholders. The use of smart contracts automates real estate transactions, enabling conditional execution without intermediaries. Furthermore, the system supports digital tokenization of real estate assets, facilitating fractional ownership and democratizing property investment for smaller investors. Federated learning is employed for sentiment analysis, aggregating decentralized social feedback to assess property desirability. Experimental evaluations demonstrate high predictive accuracy and robust system performance. This framework not only enhances the reliability and transparency of property valuation but also lays the foundation for the future of intelligent, data-driven real estate ecosystems.