The heating, ventilation, and air conditioning (HVAC) system is a critical component of energy consumption in commercial buildings. To optimize energy usage and reduce costs, it is crucial to monitor and analyze the HVAC system’s performance and its impact on occupant behavior. In this paper, we present an IoT-based monitoring and data analysis approach for HVAC energy usage and occupant behavior in commercial buildings, using statistical methods and machine learning algorithms to identify patterns and correlations. The approach involves installing temperature sensors both inside and outside the building and occupancy sensors to collect data on the HVAC system’s performance and occupant behavior. The collected data is analyzed using a variety of statistical and machine learning techniques, including linear regression, clustering, and decision trees, to identify patterns and correlations between HVAC energy usage and occupant behavior. The findings show that indoor temperature is closely related (70%) to the HVAC system’s performance, and fluctuations in outdoor temperature affect HVAC energy consumption (37%). Additionally, occupant behavior has a significant impact of around 32% on HVAC energy usage, with occupancy patterns and preferences affecting HVAC usage. The IoT-based monitoring and data analysis approach offers insights into HVAC energy usage and occupant behavior, enabling building managers to optimize energy usage, reduce costs, and enhance occupant comfort. The research also contributes to the development of more sustainable and energy-efficient commercial buildings, by providing data-driven insights into HVAC energy consumption and occupant behavior.