This dissertation presents a comprehensive examination of the integration of Machine Learning (ML) methodologies to enhance the analysis and development of subsurface geothermal resources, a pivotal element within the renewable energy spectrum. Despite geothermal energy's substantial potential for sustainable energy production, its exploitation is impeded by numerous geological and technical obstacles. This research endeavors to overcome these impediments by harnessing sophisticated ML algorithms, aiming to augment the predictability and operational efficiency in the exploration and characterization of geothermal resources. Utilizing an extensive dataset from the Northeastern United States, the study conducts a thorough analysis and refinement of prevailing approaches in data preprocessing, feature engineering, and hyperparameter optimization.