This thesis explores the development of a foundational Digital Twin (DT) to enhance user comfort and operational efficiency through real-time monitoring, building on the industry’s transition from static Building Information Modeling (BIM) to dynamic DT systems. The DT was constructed using Autodesk Revit for 3D modeling, Autodesk Tandem for visualization, and Microsoft Azure Digital Twins for data integration, utilizing real-time data from occupancy and environmental sensors via Home Assistant. The study assessed REST API and MQTT communication protocols to optimize data transmission between physical and digital entities. REST API proved more effective, supported by both sensor types, enabling seamless and secure integration. In contrast, MQTT implementation faced significant challenges, including firewall restrictions, Tandem’s lack of native support, and the need for custom Python scripting, while Azure Digital Twins integration failed due to the researcher’s limited expertise. The absence of built-in analytics in Tandem further restricted predictive capabilities, limiting long-term comfort optimization. Despite these obstacles, the DT demonstrated feasibility for real-time monitoring in educational settings, providing valuable insights into data flow and system dynamics. The findings underscore the critical need for improved interoperability, advanced analytics, and scalable sensor integration to fully realize DT potential in smart buildings. This study highlights the pivotal role of user expertise and platform compatibility in DT deployment, offering a foundation for future research to address these limitations and advance smart building technologies.