As the world confronts intensifying climate change, reducing anthropogenic carbon dioxide (CO2) emissions from industrial sources has become an urgent global priority. With global CO2 emissions surpassing 36 Gt annually, post-combustion carbon capture remains a critical strategy in climate mitigation, particularly for fossil-fuel-based industries. This study presents the dynamic modeling and advanced control of a monoethanolamine (MEA)-based CO2 absorption–regeneration system. A comprehensive process model was developed in MATLAB/Simulink, incorporating mass and energy balances, vapor–liquid equilibrium, and absorber–stripper interactions. The model was validated against established benchmark datasets to ensure reliability. To enhance control performance under variable flue gas conditions, three strategies were evaluated: conventional proportional–integral-derivative (PID) control, fuzzy logic control (FLC), and model predictive control (MPC). The control objective was to maintain a CO2 capture efficiency above 90% while minimizing energy consumption. Simulation results demonstrate that MPC achieved a 12.4% reduction in reboiler energy duty (from 3.90 to 3.42 GJ/ton CO₂) and improved dynamic response time by 37% compared to PID control. FLC exhibited strong robustness under ±10% inlet CO2 fluctuations, albeit with slower recovery dynamics. These findings highlight the potential of intelligent control strategies to significantly improve the energy efficiency and operational flexibility of solvent-based carbon capture systems. The proposed framework supports the integration of advanced control in industrial CO2 capture facilities to enable cost-effective and resilient industrial decarbonization.