This study develops a Python-based hybrid forecasting framework that integrates classical Decline Curve Analysis (DCA) with Support Vector Regression (SVR) to improve the reliability of oil production forecasting and the estimation of Estimated Remaining Reserves (ERR), which are subsequently used to derive cumulative CO₂ emission potential. Historical production data are segmented to represent boundary-dominated flow conditions, enhancing the stability and physical consistency of decline parameters. SVR is then applied to correct localized deviations in the historical response that are not captured by conventional DCA formulations, yielding improved decline representations for forecasting. The hybrid DCA–SVR model achieves a more coherent historical match and reduced average residual errors compared to standalone exponential, hyperbolic, and harmonic decline models. Using the hybrid framework, ERR estimates of 4.92×10⁵, 1.74×10⁶, and 3.62×10⁶ STB were obtained for the exponential, hyperbolic, and harmonic decline scenarios, respectively, corresponding to cumulative CO₂ emission potentials of approximately 213,036, 753,420, and 1,567,460 tons of CO₂. These scenario-based results provide a bounded range of emission outcomes, explicitly demonstrating that uncertainty in decline model selection propagates directly into long-term ERR and CO₂ emission estimates. Overall, the proposed hybrid machine learning–decline curve analysis (ML-DCA) framework offers a transparent and reproducible approach for linking production forecasting with quantitative CO₂ impact assessment in data-driven reservoir management.