Although numerical simulation exhibits high predictive accuracy in optimizing development strategies for CO₂ water-alternating-gas （CO ₂-WAG） flooding，its limited efficiency cannot facilitate high-efficiency optimization of development plans for complex reservoirs due to the enormous computational burden involved in multi-parameter combinational and global optimization. To address this issue，a surrogate modeling approach based on the random forest algorithm is proposed and integrated with a global optimization framework to greatly enhance computational efficiency and predictive accuracy. By employing an improved K-Fold cross-validation method for hyperparameter optimization，a surrogate model capable of accurately capturing the complex nonlinear relationships between input parameters and development outcomes is constructed. The reliability of the model is validated using the test set of goodness of fit（ R²） and root mean square error （RMSE），and its feasibility and precision are further demonstrated through comparisons with numerical simulation results. During the optimization process，an iterative traversal method is employed to explore the trade-off between incremental oil recovery and CO₂ storage efficiency，leading to the proposal of an optimal injection-production strategy meeting multiple objectives. The optimization results reveal that compared with conventional development schemes，the random forest algorithm optimized strategy significantly increases cumulative oil production，enhances CO₂ storage efficiency，and delays gas breakthrough. For a real tight reservoir model，the optimized scheme delivers an increase of 8.6% in cumulative oil production，an improvement of 14.5% in CO₂ storage，and a six-year delay in gas breakthrough，demonstrating significant enhancements in displacement performance and development benefits.