Accurate　and　reliable　monthly　streamflow　prediction　plays　a　crucial　role　in　the　scientific　allocation　and　efficient　utilization　of　water　resources.　In　this　paper,　we　proposed　a　prediction　framework　that　integrates　the　input　variable　selection　method　and　Long　Short-Term　Memory　(LSTM).　The　input　selection　methods,　including　autocorrelation　function　(ACF),　partial　autocorrelation　function　(PACF),　and　time　lag　cross-correlation　(TLCC),　were　used　to　analyze　the　lagged　time　between　variables.　Then,　the　performance　of　the　LSTM　model　was　compared　with　three　other　traditional　methods.　The　framework　was　used　to　predict　monthly　streamflow　at　the　Jimai,　Maqu,　and　Tangnaihai　stations　in　the　source　area　of　the　Yellow　River.　The　results　indicated　that　grid　search　and　cross-validation　can　improve　the　efficiency　of　determining　model　parameters.　The　models　incorporating　ACF,　PACF,　and　TLCC　with　lagged　time　are　evidently　superior　to　the　models　using　the　current　variable　as　the　model　inputs.　Furthermore,　the　LSTM　model,　which　considers　the　lagged　time,　demonstrated　better　performance　in　predicting　monthly　streamflow.　The　coefficient　of　determination　(R2)　improved　by　an　average　of　17.46%,　33.94%,　and　15.29%　for　each　station,　respectively.　The　integrated　framework　shows　promise　in　enhancing　the　accuracy　of　monthly　streamflow　prediction,　thereby　aiding　in　strategic　decision-making　for　water　resources　management.