This research presents the design framework of the artificial intelligent algorithm for an automated building management system. The AI system uses wireless sensor data or IoT (Internet of Things) and user's feedback together. The wireless sensors collect data such as temperature (indoor and outdoor), humidity, light, user occupancy of the facility, and Volatile Organic Compounds (VOC) which is known as the source of the Sick Building Syndrome (SBS) or New Building Syndrome because VOC are often found in new buildings or old buildings with new interior improvement and they can be controlled and reduced by appropriate ventilation efforts. The collected data using wireless sensors are post-processed to be used in the neural network, which is trained in accordance with the collected data pattern. When the users of the facility have the control of the building's ventilation system and the AI system is fully trained using the user input, it will mimic the user's pattern and control the building system automatically just as the user wants. In this research, data were collected from 4 different buildings: university library, university cafeteria, a local coffee shop, and a residential house. Fuzzy logic controller is also developed for better performance of the HVAC. Indoor air quality, temperature (indoor and outdoor), HVAC fan speed and heater power are used for fuzzified output. As a result, the framework and simulation model for the energy efficient AI controller has been developed using fuzzy logic controller and the neural network-based energy usage prediction model.