Photocatalytic water splitting offers a promising route for sustainable hydrogen production, but its efficiency remains limited by complex interactions between material properties, operating conditions, and reaction mechanisms. This study presents a data-driven approach to optimize photocatalytic water splitting for enhanced hydrogen evolution. By integrating experimental data, density functional theory calculations, and machine learning algorithms, we identified key descriptors governing photocatalyst performance. A predictive model was developed to screen optimal catalyst compositions, morphologies, and operating conditions, leading to a significant increase in hydrogen production rates. The results demonstrate the potential of data-driven optimization to accelerate the discovery of high-performance photocatalysts, paving the way for scalable and efficient solar-driven hydrogen production.