Thermophysiological comfort is a critical attribute of clothing, it refers to the ability of a garment to maintain the human body’s thermal balance across various environmental conditions, and it can be assessed through experimental methods or numerical modeling. This study utilizes CLO3D design software for virtual garment fitting, integrating the generated garments into the ABI Comfort Simulator (ABICS). Additionally, it explores the challenges of utilizing advanced 4D body scanning technologies into the Python-based package, which currently supports only MakeHuman-generated meshes. The study aims to assess thermophysiological comfort using Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD) metrics generated by the simulator across different garments, including a winter coat, long coat, sweater, and sport shirt. While the simulator provided logical and expected results for the baseline scenario without clothing, the simulations produced identical results for both clothed and unclothed models, indicating a potential issue in the fabric property implementation or solver functionality. This highlights the need for further investigation and refinement of clothing integration and heat transfer modeling to ensure accurate thermal assessments. Future work will focus on improving solver implementation, air-layer modeling, and mesh compatibility to enhance the accuracy and applicability of ABICS for thermal comfort studies.