A novel smoothing-based long short-term memory (Smooth-LSTM) framework for up to 5 days ahead flood forecasting is proposed, and compared with the benchmark LSTM (LSTM) model, an ANN model, and the conceptual MIKE11 NAM-HD (MIKE) hydrological model. This framework was tested in the typical middle Mahanadi River basin (India) having a tropical monsoon-type climatology. Variation of training loss indicated that the LSTM network has a higher learning ability at smaller network and batch sizes. The Smooth-LSTM model could predict the streamflow with higher Nash-Sutcliffe efficiency of 0.82–0.87 up to 5 days lead-time with a better reproduction of the observed crucial high peak floods; whereas the corresponding MIKE, ANN and LSTM model-based forecasts were acceptable only up to 4, 3 and 1-day lead-times, respectively. Overall, the Smooth-LSTM model is found to be robust in operational flood forecasting characterized with lower uncertainty and the least sensitivity to any redundant input information.