Growing energy consumption from campus infrastructure including lecture halls that run heating, ventilation and air conditioning (HVAC) systems motivates data-driven optimization. This research demonstrates an integrated application of Internet of Things (IoT) sensors and cloud-hosted predictive data analytics to enable smart lecture room policies improving efficiency and sustainability. A Raspberry Pi Pico W IoT device was interfaced with BME680 sensor for temperature, humidity and air quality data. The device also incorporated a PIR sensor for occupancy detection and Wi-Fi connectivity to transmit multivariate time series data. The prototype was installed in a university lecture room for real-time data capture. Data was directed to a cloud analytics pipeline including MySQL storage and Node-RED for pre-processing. Time series forecasting was conducted by training autoregressive integrated moving average (ARIMA), Prophet and machine learning models on historical data to predict temperature, occupancy levels, and usage patterns 24 h into the future. An interactive dashboard visualized both real-time streams and model forecasts using Grafana for analytical insights.