The accuracy of monthly runoff predictions is crucial for decision-making and efficiency in various areas, such as water resources management, flood control and disaster mitigation, hydraulic engineering scheduling, and agricultural irrigation. Therefore, in order to further improve the accuracy of monthly runoff prediction, aiming at the problem that the traditional Stacking ensemble method ignores (the base model correlation between different folds in the prediction process), this paper proposes a novel Stacking multi-scale ensemble learning model (SWA–FWWS) based on random weight averaging and a K-fold cross-validation weighted ensemble. Then, it is evaluated and compared with base models and other multi-model ensemble models in the runoff prediction of two upstream and downstream reservoirs in a certain river. The results show that the proposed model exhibits excellent performance and adaptability in monthly runoff prediction, with an average RMSE reduction of 6.44% compared to traditional Stacking models. This provides a new research direction for the application of ensemble models in reservoir monthly runoff prediction.