Hydrological modeling is vital for assessing land use and land cover (LULC) impacts on water balance in river basins, yet data scarcity in transboundary regions like the Ruvu River Basin (RRB), shared by Tanzania and Kenya, poses challenges. This study integrates satellite precipitation to bridge data gaps and quantify LULC effects using the SWAT model. The RRB (5512.54 km2) was delineated into 16 sub-basins using a DEM in ArcGIS. The Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Global Precipitation Measurement (GPM) precipitation datasets from 2000 to 2023 were compared against sparse gauge data using statistical indices. CHIRPS outperformed GPM and was merged with gauge data for SWAT inputs in conjunction with climate, soil data, and LULC maps (2001, 2012, 2023) from Landsat imagery. Classification results showed a 3.56% forest decline and an 11.41% agricultural increase. Daily SWAT calibration (2008–2015) and validation (2016–2019) yielded strong performance (calibration: R2 = 0.82, NSE = 0.75, bias = −5.2%; validation: R2 = 0.88, NSE = 0.79, bias = −6.8%). LULC changes drove an 11.31% rise in surface runoff and 3.85% in water yield, linked to deforestation and urbanization, while evapotranspiration and groundwater flow dropped 2.33% and 15.13% due to reduced vegetation. These results highlight the need for sustainable LULC management in the RRB to mitigate hydrological risks. Reforestation, land-use zoning, and improved transboundary data-sharing between Tanzania and Kenya are recommended to balance development and ecological resilience.