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Thermal comfort is a significant factor in the indoor building environment because it influences both human productivity and health. A currently popular method for predicting thermal comfort levels, the Predicted Mean Vote (PMV) and Predicted Percent Dissatisfied (PPD) model, unfortunately, has certain limitations. Consequently, the development of a better method for making accurate predictions (especially for individuals) is needed. Our goal was to develop a tool to predict individual thermal comfort preferences and automatically control the heating, ventilation, and air conditioning (HVAC) systems. This study adopted a series of human-subject experiments to collect essential data. All collected data was analyzed by adopting different machine learning algorithms. The machine learning algorithms predicted individual thermal comfort levels and thermal sensations, based on facial skin temperatures of participants in the experiments. These predictions were input data for the HVAC system control model, and results supported the potential for using facial skin temperatures to predict thermal comfort and thermal sensation levels. Moreover, this tool provided automatic control of the HVAC systems that can help improve the indoor environment of a building.