Indoor environmental conditions, particularly air temperature and CO₂ concentration, play a central role in shaping comfort, health, and energy performance in academic buildings. Despite the widespread deployment of IoT-based monitoring systems, most HVAC systems still operate reactively. This thesis addresses this gap by developing an explainable, data-driven forecasting framework that predicts short-term indoor environmental trends and integrates these predictions into a 3D BIM-based visualization environment for improved decision support. Using one year of sensor data from the Aule R building at Politecnico di Torino, the study evaluates the performance of both machine-learning and deep-learning methods, including Random Forest, CNN, and LSTM for modeling temperature and CO₂ dynamics.