The expected result is a functional bidirectional Digital Twin prototype implemented for a real academic building. The system will establish structured data pipelines connecting BIM models (architectural and electromechanical) with live IoT sensor streams and BMS platforms. A middleware layer will enable semantic mapping between physical devices and BIM elements, ensuring interoperability and traceability. The research will demonstrate: 1. Real-time visualization of environmental and operational parameters within a BIM-based 3D environment. 2. A classification framework distinguishing Digital Model, Digital Shadow, and Digital Twin maturity levels. 3. AI-driven predictive models for HVAC optimization and anomaly detection. 4. A closed-loop mechanism allowing control commands from the digital interface back to physical systems. Performance will be evaluated based on latency, data consistency, scalability, and energy efficiency improvements. The framework aims to provide a reusable architecture adaptable to other buildings. The final deliverable will include a validated architectural model, integration methodology, and performance evaluation demonstrating measurable operational improvements and improved decision support capabilities for facility managers.