Abstract: The prediction of runoff into the reservoir has important indications for the operation of Danjiangkou Reservoir and the utilization of water resources. Based on the gray wolf optimization algorithm (GWO), we constructed different prediction models, carried out the study of reservoir runoff prediction in Danjiangkou, made the selection of network structure hyperparameters, and verified the characteristics of global traversability and fast convergence of GWO. The results show that the prediction accuracy and generalization performance of the GWO-LSTM model are better than those of the GWO-BP model and the stepwise regression model. The Nash efficiency coefficient of its verification period reaches 0.969 on average. The overall trend forecast is good, and the peak capture is slightly insufficient, which is suitable for the prediction of monthly inflow to the Danjiangkou reservoir. The prediction results of model hyperparameters based on empirical values are not as good as GWO. Based on empirical values, the NSE of the verification period is less than 0.5, which does not reach the acceptable range, and there is a certain contingency. It is recommended to select an optimization algorithm with global optimization characteristics for hyperparameter selection. The global traversability and fast convergence characteristics of the GWO algorithm are verified, and the convergence state can be reached after 3 iterations on average.