This study aims to investigate land use/land cover (LULC) and climate change for a better understanding of the hydrological processes of the Bhavani watershed, India. The ANN-CA model, which is based on artificial neural networks and cellular automata, is utilized for the simulation and prediction of LULC. Five criteria, including DEM, slope, aspect, distance from the road, and distance from existing built-up areas, were used as exploratory data for the learning process of the ANN-CA model. The calibrated LULC maps for 2020 showed a high level of agreement, with a kappa index of 0.76 and a percentage of correctness 78.23%. The model was then used to predict LULC changes for the years 2030, 2040, and 2050 and integrated these predictions with different future climate scenarios (CMIP6 RCP4.5 and RCP8.5) to estimate changes in hydrological components using the soil and water assessment tool (SWAT). The average sediment yield ranges from 9.86 to 14.79 ton/ha/year between 2000 and 2020, which is attributed to the changes in LULC. Additionally, the combination of climate change scenarios and changes in LULC resulted in the projected increases in annual average soil losses by 23.90 and 20.18 ton/ha/year in 2030; 18.22 and 25.07 ton/ha/year in 2040 and 23.87 and 23.54 ton/ha/year in 2050. Among the 26 sub-watersheds, SW-07, SW-09, SW-21, SW-22, SW-25, and SW-26, showed high sediment yield in the watershed. The model calibration and validation values of R2, NSE, PBIAS, and RSR showed that the predicted flow and sediment yield results are in good agreement with the observed values.