Accurate discharge forecasting is crucial for effective water resource management, flood risk mitigation, and hydrological planning, particularly in regions prone to extreme weather events. This study evaluates the performance of a Long Short-Term Memory (LSTM) network in predicting river discharge at the Hoa Duyet hydrology station. The prediction model is developed using rainfall data from the Ngan Sau river basin, collected over a 49-year period from 1975 to 2023. The model's accuracy was assessed across a range of lead times (1-day, 3-day, 5-day, and 7-day) and time lag length (365, 90, 30, 10, and 7 days). It was revealed that short-term forecasts (e.g., 1-day) consistently achieved high accuracy, with the time lag length 90-day yielding the best Nash-Sutcliffe Efficiency (NSE) of 0.864. Seasonal analysis indicated the reliability of the model for the rainy season (NSE = 0.863), but lower accuracy during the dry season (NSE = 0.582), reflecting the challenges of predicting low-flow dynamics. The model also demonstrated reasonable accuracy in predicting annual runoff peaks, with an average error of 91.75 m³/s, although discrepancies were observed in specific years. These findings highlight the LSTM model's capacity to adapt to diverse temporal configurations and hydrological conditions, making it a valuable tool for discharge prediction while emphasizing the need for further optimization in low-flow and extreme event scenarios