The management of water resources relies on accurate predictions of hydrological parameters. In addition, the efficacy of any hydrological model is dependent on the precision of the input data. Researching the effects of LULC on the Savitri River’s hydrology in Raigad, Maharashtra, is the primary goal of this project. In order to achieve this, a trustworthy agro-hydrological model called SWOT (Soil and Water Assessment Tool) will be employed. The research project relied on meteorological data provided by IMD Pune for the years 2000–2020. We utilised ArcSWAT’s maximum likelihood image classification method to categorise the LULC data from the Bhuvan website into five different land use patterns. Following delineation, the 580 sq. km. study area is processed using the SWOT model, which results in the division of the entire area into 42 sub-basins and 212 HRUs. From 2003–2010, the SWOT model is calibrated using data collected from HDUG group Nasik and used in the SWAT-CUP tool. From 2011 to 2015, the model is validated. To determine how sensitive the work is, statisticians use the NSE and R2 metrics. We discovered that the NSE values in validation were 0.78 and in calibration they were 0.74. The R2 values for validation and calibration are 0.84 and 0.81, respectively, in the same vein. The SWOT model is supported by the combined observed and simulated data, which show a high degree of fitness. The results show that there was a 5% drop in undeveloped land and an 8% conversion of farmland to housing between 2000 and 2020. The study found that when homes and infrastructure are built, the LULC pattern changes. This changes bring about scary things like flash floods, more runoff, less infiltration, and lower groundwater tables. Landslides and sedimentation in bodies of water are also caused by these scenarios. Policymakers and water managers can use the study’s findings to better plan for sustainable agriculture and water resource management, and to mitigate the effects of changed LULC.