Integrating Machine Learning with the SWAT Model Across Watershed Environmental Processes: A Comprehensive Review

Watersheds are the units through which land use, climate, and human activity shape water quantity, water quality, and ecosystem health. The Soil and Water Assessment Tool (SWAT) provides a physically based, water-balance-closing simulation of the runoff, sediment, nutrient, water quality, and groundwater processes that underpin watershed environmental assessment, but its skill is bounded by strongly nonlinear, sparsely observed, and out-of-record processes. Machine learning supplies the nonlinear flexibility SWAT lacks, yet offers no guarantee of physical consistency. This review provides a comprehensive synthesis of the body of work coupling the two paradigms, drawing on 167 studies from Web of Science and Scopus. The synthesis is organized along three axes: the environmental processes assessed, the frameworks that join the two models, and the regional distribution of applications. Coupling adds most value for streamflow and other data-rich targets, and least at hydrological extremes and in groundwater, where machine learning increasingly substitutes for missing process knowledge. Integration ranges from parallel benchmarking through error correction and surrogate emulation to physics-informed embedding, with sequential designs dominating practice. Persistent challenges of extremes, non-stationarity, data scarcity, physical consistency, and reproducibility point toward physics-informed, uncertainty-aware, and transferable coupling for watershed environmental modeling and management.