Thermal comfort is an important factor in evaluating indoor environmental quality. However, accurately evaluating thermal comfort conditions is challenging owing to the lack of suitable methods for measuring individual factors such as the metabolic rate (M value). In this study, a M value evaluation method was developed using deep learning. The metabolic equivalent of task was measured for eight typical indoor tasks based on the ASHRAE Standard 55 (lying down, sitting, cooking, walking, eating, house cleaning, folding clothes, and handling 50 kg books) in 31 subjects (males: 16; and females: 15); the measurements were analyzed in terms of gender and body mass index (BMI). The experimental results were assessed using the reliability of the measured data, the M value difference in terms of gender and BMI, and the measurement accuracy. We developed a M value self-evaluation model using artificial intelligence, which achieved an average coefficient of variation (CV) of 12％. A third-party evaluation model was used to evaluate the M value of one subject based on the learning data acquired from the other 30 subjects; this model yielded a low CV of 54％. For high-activity tasks, males generally had higher M values than females, and the higher the BMI was, the higher was the M value. Contrarily, for low-activity tasks, the lower the BMI was, the higher was the M value. The breakthrough M value evaluation method presented herein is expected to improve thermal comfort control.