This study delves into the utilization of artificial intelligence (AI) for predicting the performance of Hemispherical Solar Stills (HSS), aiming to transcend reliance on empirical methods. Employing real-world experimental data, five distinct prediction models were developed. The investigation revealed that the DT model emerged as the most efficacious in estimating both hourly productivity and instantaneous efficiency, showcasing remarkable accuracy and efficiency in its predictions. Statistical metrics demonstrated that the DT model's estimates for HSS productivity and efficiency were closely aligned with unity. This research accentuates the transformative potential of AI models in streamlining and enhancing predictive techniques for estimating HSS performance. The findings advocate for the adoption of AI-driven approaches as cost-effective and precise solutions for researchers and practitioners within the realm of mechanical engineering. By substantiating the efficacy of the DT model, this study contributes valuable insights that not only advance our understanding of AI applications in solar technology but also offer practical tools for optimizing the design and performance assessment of Hemispherical Solar Stills. The integration of AI in this context promises to usher in a new era of efficiency and accuracy, addressing challenges in the field and fostering advancements in sustainable energy technologies.