In the face of increasing availability of global thermal comfort datasets, there remains a critical gap in how personal, contextual, and environmental variables are comparatively analyzed across cultural and regional contexts. This study presents a data-driven qualitative review of 88 thermal comfort studies drawn from the ASHRAE Global Thermal Comfort Database and the Chinese Thermal Comfort Dataset, focusing on the depth and consistency of parameter usage in existing literature. Studies were systematically evaluated along 15 variables grouped into personal characteristics, contextual parameters, and PMV model inputs, assigning scores on a scale from 0 to 3 to reflect their level of consideration. Our findings reveal that personal parameters such as height and weight are the least reported (75.6% of studies omitted them), while contextual parameters like season and operation mode received greater attention. Notably, only 34.9% of studies fully account for metabolic rate variation in PMV modeling, while MRT is assumed rather than measured in over 55% of reviewed cases. Using the Chinese dataset, we also derive region-specific adaptive comfort models, with regression slopes ranging from 0.196 (severe cold zone) to 0.819 (hot summer/warm winter zone), underscoring significant geographic variation in thermal adaptation. These findings not only support the improvement of comfort modeling but also inform more energy-efficient, renewables-aligned HVAC control strategies. This review highlights the lack of standardized classification systems as a key barrier to integrated analysis. We advocate for harmonized parameter definitions and expanded demographic coverage to enhance the accuracy and cultural relevance of future comfort models.