Conventional remote sensing faces challenges in regions characterized by complex terrain, frequent cloud and fog cover, image scarcity, and mixed pixels. This study develops an integrated framework that sequentially combines spatiotemporal fusion, water masking, feature engineering, and machine learning to enable long-term SSC retrieval. Key results include: (1) RASTFM achieved the highest spectral fidelity among four fusion algorithms; (2) development of an adaptive machine learning model for independent hydrological stations and a water body masking strategy; (3) mainstream SSC decreased by 21.2% temporally and a 93.8% longitudinally from 2006 to 2020, generating 15-year along-channel mainstream SSC data for the Three Gorges Reservoir area.