Differences in the persons’ individual parameters such as age, gender, weight, height and basal metabolic rate have a significant effect on the human body thermoregulation. Therefore, using the human thermal models that developed on the basis of large humanity population cannot lead to accurate results for specific individuals. Because, the individual parameters have not been considered in standard thermal comfort models and also available individual and local models are so complicated in applications; nowadays, the necessity of developing a simple and accurate individualized model is felt. In this study, some physiological parameters such as: body fat percentage, subcutaneous fat layer thickness, body heat capacity coefficient and tissue conductive resistances have been modeled from readily-available external measurement of individuals and these parameters are incorporated into three node-model algorithm structure to predict individual variations in thermal response between individuals. Three-node thermal comfort model is based on Gagge’s standard model that has been accurately estimated thermal sensation of the bare and clothed parts of the body. The model has been verified against the analytical and experimental results where a good agreement was found. In conclusion, the results indicate that the mean error in prediction of skin temperature is decreased from 1.2℃ for three-node model to 0.4℃ for the new individual model.