<p>Machine learning (ML) techniques have gained popularity in watershed management and planning due to their accurate forecasting capabilities. The study aims to evaluate and compare the performance of various machine learning models for monthly runoff forecasting in the Thale Sap Songkhla Basin and assess their effectiveness in improving runoff prediction using predicted rainfall as input. The data utilized in this study included hydrological and meteorological data (i.e., runoff, rainfall, relative humidity, air temperature, and wind speed), as well as large-scale climate indicators (LSCI), namely the Southern Oscillation Index, sea surface temperature, and the Indian Ocean Dipole Mode Index. Statistical performance metrics such as the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and overall index (OI) between the observed and forecasted data were employed to evaluate and compare the performances of the ML models. The results indicate that meteorological and LSCI, particularly sea surface temperature, are crucial for monthly rainfall and runoff forecasting. The comparative results showed that the SVR-rbf model exhibited better performance in monthly rainfall forecasting at 5 out of 12 stations. The validation results also revealed that the SVR-rbf and RF models exhibited the highest performance for forecasting runoff at 4 stations each out of 8, for the Thale Sap Songkhla basin at most stations. Adjusting additional parameters for RF models, such as bagSizePercent, improved model performance. Additionally, using forecasted rainfall as an input for runoff prediction improved model performance at 7 out of 8 stations, with accuracy gains of up to 42.34%.</p> Graphical Abstract <p>This graphical abstract outlines a workflow for forecasting runoff in the Thale Sap Songkhla Basin, Thailand. Background and conceptual framework demonstrate whether the use of rainfall forecasts for runoff forecasting improves model performance. The data utilized in this study included hydrological and meteorological data (i.e., runoff, rainfall, air temperature, wind speed, and relative humidity), as well as large-scale climate indicators (LSCI), namely the Southern Oscillation Index, sea surface temperature, and the Indian Ocean Dipole Mode Index. This study evaluates and compares the performance of various machine learning techniques, including M5 model tree (M5), random forest (RF), support vector regression with polynomial (SVR-poly), support vector regression with radial basis function kernels (SVR-rbf), and multilayer perceptron (MLP). These indicators include the correlation coefficient (r), mean absolute error (MAE), root mean squared error (RMSE), and overall index (OI), which were used to assess the model’s effectiveness. It begins with predicting rainfall at 12 rain gauge stations. The forecasted rainfall is then used as input for predicting runoff at 8 stations. Both models follow the same methodology. Distinct colors are used in the figure to highlight these differences for clarity. In conclusion, meteorological variables and LSCI play a crucial role in monthly rainfall and runoff forecasting. Among LSCI, SST stands out as a key factor. SVR-rbf performed best for rainfall forecasting, while SVR-rbf and RF excelled in runoff forecasting for the Thale Sap Songkhla basin. A highlight of this study is that it found that incorporating forecasted rainfall as an input can improve runoff forecasting accuracy.</p>