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Accurate prediction of CO2storage mass and cumulative oil production is critical in the context of combining subsurface carbon capture with enhanced oil recovery (CCS-EOR). This study introduces a novel committee machine-learning Gaussian Process Regression (CML-GPR) model, which integrates three well-established machine-learning algorithms—Random Forest (RF), Multi-Layer Extreme Learning Machine (MELM), and Generalized Regression Neural Network (GRNN). The combination of these models leverages the strengths of each algorithm: RF captures nonlinear relationships, MELM enhances computational efficiency, and GRNN provides smooth, generalized predictions. By integrating these complementary techniques, the CML-GPR model demonstrates significant improvements in predictive accuracy over individual models, addressing limitations in their performance. The model predicts CO2storage mass and cumulative oil production based on nine key reservoir input variables, including depth, porosity, and CO2injection rate, among others. Utilizing a large dataset of 21,193 data points from reservoir simulations, a Mahalanobis distance-based outlier detection method further refines the input data quality. The CML-GPR model achieves root mean square error (RMSE) values of 0.49 million metric tons for CO2storage mass and 13.68 million barrels for cumulative oil production, significantly outperforming individual models. The CML-GPR model provides a robust tool for optimizing CO2storage capacity and oil recovery, with practical implications for real-world reservoir management, ensuring more efficient and reliable CCS-EOR operations. This study represents a pioneering advancement in predictive modeling, offering valuable insights for optimizing both CO2storage and enhanced oil recovery in complex reservoirs.