| Mathematical models of sediment transport, grounded in physical governing equations, are essential tools for understanding sediment transport processes and supporting basin management and hydraulic engineering. However, conventional numerical methods are often constrained by computational efficiency and thus struggle to meet the demands of emerging scenarios—such as digital twins—for real-time, high-efficiency prediction and intelligent decision-making. To address this issue, this study reviews three technical routes—data assimilation, data-driven neural networks, and physics-driven neural networks—within an analytical framework that spans input boundaries and parameters, governing equations, and model outputs. The results show that: (1) data assimilation effectively suppresses the propagation of errors in initial and boundary conditions, thereby improving the stability and accuracy of forecasts; (2) data-driven neural networks can markedly reduce computation time and are suitable for short-term, rapid prediction tasks; and (3) physics-driven neural networks provide improved physical consistency under data-scarce conditions, yet still face challenges related to convergence and accuracy in high-dimensional, multi-scale problems. On this basis, a fusion-oriented approach for engineering applications is proposed, which centers on hybrid physical–data modeling combined with online correction through data assimilation, with the aim of balancing efficiency and accuracy and providing a useful reference for real-time updating and intelligent decision-making in digital-twin sediment–water systems. |