Abstract: Reservoir operation rules are the fundamental basis for the daily operation of a reservoir. However, existing methods for extracting reservoir operation rules have deficiencies in accurately capturing the information from optimal operation models. To ensure the safety of flood protection objects upstream and downstream of the Three Gorges Reservoir during the flood season, a multi-objective optimization operation model that considers the flood control tasks of the upstream and downstream areas was constructed. Four machine learning models, namely Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Radial Basis Function Neural Network (RBFNN), and Convolutional Neural Network (CNN), were employed. These models were combined with four signal decomposition methods—Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), and Empirical Wavelet Transform (EWT)—to preprocess the model inputs (inflow and outflow) by decomposition. This approach was used to establish a machine learning-based method for extracting the operation rules of the Three Gorges Reservoir. The application results at the Three Gorges Reservoir show that coupling signal decomposition methods with machine learning models can significantly improve the simulation accuracy of outflow. Compared to using machine learning models alone, the coefficient of determination (R²) for the decision variables can be improved by more than 0.1. Specifically, machine learning models coupled with EEMD and EWT decomposition can increase the R² of the decision variables by approximately 0.3. The model coupled with EWT outperforms the other three decomposition models in simulating the operation scheme and is closer to the Pareto front of the optimal operation. This research provides an effective hybrid extraction paradigm for the intelligent extraction of operation rules for complex reservoir systems.