In the wake of increasing global warming, intensifying the effects of climate change, and the spread of phenomena such as urban heat islands, the topic of thermal comfort in urban open spaces has been raised as one of the key issues in sustainable urban planning and design. The aim of this research is to analyze the structure and content of studies conducted in the field of urban thermal comfort with a comparative approach between domestic and international research in the period 1399 to 1404 (corresponding to 2020 to 2025). To achieve this goal, 145 articles, including 97 international articles and 48 Persian articles, were selected and reviewed using the PRISMA systematic methodological framework. The results of the analyses show that at the international level, research has increasingly moved towards the use of data-driven algorithms and machine learning models such as XGBoost, LightGBM, Random Forest, and deep neural networks. These studies, focusing on topics such as urban climate, spatial morphological patterns, physiological characteristics of individuals, remote sensing, and multiscale data analysis, have provided accurate prediction models that support urban decision-making. In contrast, domestic research is more based on experimental studies, field observations, and traditional statistical analyses, and uses indicators such as PMV, PET, and UTCI to measure comfort, while placing great emphasis on Iran's specific climatic conditions. These differences indicate a significant gap in the level of methodology, tools, and scope of analysis between domestic and international studies. By identifying macro trends, existing gaps, and research opportunities, this study emphasizes the need to integrate local experiences with advanced global modeling capabilities and provides perspectives for improving the quality of thermal comfort studies in Iran.