Accurate streamflow forecasting plays a vital role in water resource engineering, management, and planning. This study evaluates the performance of the Group Method of Data Handling (GMDH) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in predicting daily streamflow. Rainfall and streamflow data, collected from rain gauges and hydrological stations in the upstream area of the Kone River in Binh Dinh Province, were used as inputs for the models and tested across various input scenarios. Model performance was assessed using three statistical metrics: the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The results revealed that the ANFIS model consistently outperformed the GMDH model, achieving the highest R² value of 0.94 and the lowest RMSE (64.6 m³/s) and MAE (14.2 m³/s). Additionally, Scenario 1 demonstrated the best predictive performance across both models. This study successfully developed reliable approaches for daily streamflow forecasting and provided valuable insights into the influence of input variables on prediction accuracy.