This study evaluated the performance and hydrologic utility of four different satellite precipitation datasets (SPDs), including GPM (IMERG_F), PERSIANN_CDR, CHIRPS, and CMORPH, to predict daily streamflow and SL using the SWAT hydrological model as well as SWAT coupled soft computing models (SCMs) such as artificial neural networks (SWAT-ANNs), random forests (SWAT-RFs), and support vector regression (SWAT-SVR), in the mountainous Upper Jhelum River Basin (UJRB), Pakistan. SCMs were developed using the outputs of un-calibrated SWAT models to improve the predictions. Overall, the GPM shows the highest performance for the entire simulation with R² and PBIAS varying from 0.71 to 0.96 and 13.1 to 0.01%, respectively. For the best GPM-based models, SWAT-RF showed a superior ability to simulate the entire streamflow with R² of 0.96, compared with the SWAT-ANN (R² = 0.90), SWAT-SVR (R² = 0.87), and SWAT-CUP (R² = 0.71). Similarly, SWAT-ANN presented the best performance capability to simulate the SL with an R² of 0.71, compared with the SWAT-RF (R² = 0.66), SWAT-SVR (R² = 0.52), and SWAT-CUP (R² = 0.42). Hence, hydrological coupled SCMs based on SPDs could be an effective technique for simulating hydrological par-ameters, particularly in complex terrain where gauge network density is low or uneven.