Abstract Streamflow forecasting is crucial for planning, designing, and managing water resources. Accurate streamflow forecasting is essential in developing water resource systems that are both technically and economically efficient. This study tested several machine learning techniques to estimate monthly streamflow data in the Hunza River Basin, Pakistan, using streamflow, precipitation, and air temperature data between 1985 and 2013. The techniques tested included adaptive boosting (AB), gradient boosting (GB), random forest (RF), and K-nearest neighbors (KNN). The models were developed using river discharge as the target variable, while air temperature and precipitation as the input variables. The model's performance was assessed via four statistical performance indicators namely root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). The results obtained for RMSE, MSE, MAE, and R2 using AB, GB, RF, and KNN techniques are (16.8, 281, 6.53, and 0.998), (95.1, 9,047, 61.5, and 0.921), (126.8, 16,078, 74.6, and 0.859), and (219.9, 48,356, 146.3, and 0.775), respectively. The results indicate that AB outperforms GB, RF, and KNN in predicting monthly streamflow for the Hunza River Basin. Machine learning, particularly AB, offers a reliable approach for streamflow forecasting, aiding hazard and water management in the area.