There is an increasing need to address the issue of achieving and maintaining a healthy and comfortable indoor environment in healthcare facilities, such as hospitals, nursing homes, etc. The occupants can identify the environmental variables that contribute to the indoor quality; however, different people would have their individual needs, health conditions, preferences, and expectations of the environment. An individualized comfort model is a new approach to enhance the occupants’ comfort in a monitored micro-environmental condition. The proposed individualized comfort model in this work integrates three primary types of input parameters: micro-environmental data, individual physiological signals, and individual-specific data. This paper intends to form the framework of the individualized indoor comfort model by leveraging wearable sensors and data-driven methods that address the real-time data collection and monitor. This comfort model is more about the thermal and visual comfort level at this stage. The measurement methods of the input, output, and third factors (e.g., confounding and mediating variables) will be discussed. The hardware (Arduino platform, wristband, camera module) and the software (smartphone application, webserver) are proposed in this real-time individualized comfort monitor system. Also, the modeling workflow to develop such personalized comfort models will be explained. This developed personal comfort model with long-term input data is expected to have a more accurate prediction accuracy.