Buildings in Hong Kong consume about 90% of total electricity. To meet decarbonization goals, commercial buildings must reduce electricity usage by 30–40% by 2050. This requires building owners to set ambitious energy-saving goals to improve energy efficiency and conservation (EE&C), especially for air conditioning, which is the largest energy consumer. Innovation and new technologies are essential for this transition. Cooling load prediction is crucial for optimizing air conditioning efficiency, influenced by building design, weather, and internal heat gains from occupant activity. The project team developed a 5G-based Cooling Load Prediction Information System for central air conditioning in commercial buildings, using Multiple Linear Regression and Autoregressive models. It leverages data from 5G webcams and weather information from the Hong Kong Observatory. A pilot installation at a flagship shopping mall is projected to save over 4% on the waterside and 13% on the airside. The system also monitors occupant flow for demand-controlled ventilation, offering a cost-effective alternative to traditional CO2 sensors. With minimal operational costs from public data sources, this system is easily extendable to other buildings. Successful in achieving energy savings and decarbonization, it can integrate with BACnet control systems for wider application.