The growing demand for clean and renewable energy sources has intensified the need to identify and exploit geothermal resources as a key solution for sustainable energy development. However, geothermal exploration faces significant challenges including geological complexity, high drilling costs, economic risks, and spatial data limitations. This study evaluates the efficiency of advanced machine learning algorithms, specifically Random Forest and Generative Adversarial Networks (GANs), in identifying geothermal energy potential areas in Akita and Iwate provinces, Japan. Using a limited dataset of 152 geothermal well locations, seven key parameters were analysed: volcanic activity, fault and fracture density, hot springs, surface thermal indices, fumaroles, mud volcanoes, and surface alteration evidence. Data were collected from geological and remote sensing sources and pre-processed for modelling. Results demonstrate that both algorithms effectively identify high-potential areas despite data scarcity. Random Forest achieved 94.08% accuracy in well identification with a C/S(C) index of 10.93, demonstrating robust performance and spatial correlation. The Generative Adversarial Network showed superior performance with 96.71% accuracy and a C/S(C) index of 4.36, indicating exceptional capability in identifying geothermal potential areas and detecting complex spatial patterns. These findings confirm that hybrid approaches combining machine learning and deep learning, particularly GANs, possess high capability for accurate geothermal prospectivity mapping and can effectively overcome limitations posed by data scarcity, providing valuable tools for exploration prioritization and investment decision-making.