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Carbon capture and storage (CCS) has emerged as a potential method for reducing greenhouse gas emissions. Effective management of CO2 storage sites is essential for ensuring long-term environmental benefits and economic viability. This paper presents an innovative approach to CO2 storage modeling by integrating deep learning techniques. The proposed method integrates an advanced deep learning framework into CO2 storage modeling, utilizing a range of algorithms to predict the behavior and outcomes of carbon capture and storage. This integration significantly enhances the accuracy of evaluating CO2 storage efforts. Experimental results on the primary dataset showcase compelling outcomes. The framework achieves remarkable RMSE (0.03042) and R2 (0.9998) values using the VNet model, underscoring its capability to provide insightful forecasts of CO2 plume behavior and reservoir responses under diverse operational conditions. The framework enhances model robustness and facilitates application in dynamic CO2 storage environments by incorporating real-time monitoring data and uncertainty quantification techniques. These results exemplify the transformative potential of integrating advanced deep learning methodologies into CO2 storage modeling, offering actionable insights for effective greenhouse gas emissions mitigation.