Lake Victoria, the world’s second-largest freshwater lake, sustains over 200 million people while critically modulating regional climate. While conventional TWSA estimation methods rely on either (1) limited GRACE/GRACE-FO observations (2002–present) or (2) purely physics-based hydrological models with high parameter uncertainty, this chapter develops an AI hybrid approach that overcomes both limitations. Our framework uniquely integrates a multi-layer perceptron with combined gridded/basin-averaged loss functions to reconstruct TWSA for 1971-2022 using precipitation and lake level data. Compared to traditional methods, the AI-reconstructed product demonstrates three key advantages: (1) 50-year coverage (vs. GRACE’s 20-year record), (2) elimination of process-model parameterization errors, and (3) explicit separation of anthropogenic and climate signals through data-driven pattern recognition. Validation shows superior alignment with observed lake levels (RMSE improvement versus best physical model) while maintaining water budget closure. This work provides the first comprehensive TWSA reconstruction for the Lake Victoria Basin, bridging critical gaps in long-term hydrological monitoring.