Surface methods, including gravity, magnetic, electrical, seismic, and electromagnetic surveys, are used to identify the cap rock, reservoir zones, and fluid pathways. Borehole geophysics is used to assess formation properties and temperature profiles. More recently, data from geological and geophysical surveys have been used to develop synthetic subsurface models, enhancing the understanding and visualization of geothermal systems [7; 8; 9]. Additionally, a two-dimensional electrical conductivity model derived from magnetotelluric data has been utilized to reveal electrically anisotropic features associated with tectonic deformation and magmatic intrusions. This model has identified a deeper hydrothermal system above a magmatic structure, providing valuable insights for future geothermal exploration [10]. In recent years, the integration of artificial intelligence (AI) into geothermal energy exploration has garnered significant attention for its potential to improve the efficiency and accuracy of geothermal resource assessment. AI offers innovative solutions for processing and interpreting complex datasets, thereby improving the efficiency and accuracy of geothermal energy evaluations. [PERSON] et al. [9] investigated the role of AI in geothermal resource exploration, highlighting its potential to improve efficiency, data interpretation, and prediction accuracy, key factors in advancing sustainable renewable energy solutions. [PERSON] et al. [11] presented an AI-based approach for clustering and classifying geothermal reservoirs in the Ying-Qiong Basin, China. By applying AI algorithms to geological and geophysical data, the authors identified patterns and grouped reservoirs with similar characteristics. Their study demonstrated the potential of AI to automate and refine the classification process, providing valuable insights for more effective reservoir management and resource utilization. The novelty and primary contribution of this study lie in integrating spatially variable magnetic susceptibility, seismic, and geochemical data, while employing radiogenic heat production (RHP) as a key indicator for identifying geothermally favorable sites. This integration is a necessary step, as the critical importance of seismic and geochemical data in assessing geothermal potential has been well-documented [12; 13]. In addition, RHP is closely linked to the concentration of heat-producing elements, primarily U, Th, and K, which are significant contributors to subsurface thermal anomalies. Accordingly, RHP-based methods have been widely employed for the identification of geothermal heat sources and the preliminary assessment of geothermal energy potential [14; 15; 16; 17; 18; 19; 20; 21; 22]. In this study, we also introduce a novel modeling framework that uses AI-based and statistical clustering algorithms to enhance prediction accuracy in locating potential geothermal-rich zones. The predictive performance of these models is tested on Precambrian-age migmatic genisses of the northern Menderes Massif, located within the Simav Basin in western Turkiye. Furthermore, we examine the spatial relationships between AI-predicted geothermal sites and earthquake epicenters to explore potential links between tectonic activity and the localization of geothermal resources.