Mitigation of carbon emissions is an important step to achieve the climate change goals. Amine-based post-combustion CO2 capture (PCC) process is a promising technology, and many commercial projects have been developed based on different capture mechanisms governing in various carbon capture and storage (CCS) processes. The thermally regenerative amine-based PCC is a traditional technology, which consists of an absorber to capture CO2 from the flue gas and a desorber to strip CO2 from the CO2-rich. Although there have been substantial improvements in the industrial applications of amines technology, further developments are still required owing to significant energy requirement, high capital cost, and amine degradation. One of the most critical issues in the amine-based PCC process is the degradation of solvent, which occurs by the transformation of amines into other chemical components by thermal degradation and oxidative degradation. In the thermal degradation, the amines react with CO2 to form com-pounds having high molecular weight, and in the oxidative degradation, the amines react with O2 to synthesize compounds having low molecular weight. In addition, the high stable salts are formed as a result of the reaction between the amines and the carboxylic acids. These high stable salts lead to considerable problems in the regeneration process, and increase the chance of corrosion in the process equipment. Monoethanolamine (MEA) is the most recognized solvent, which is considered a benchmark solvent in the solvent-based PCC processes. It has been confirmed that to absorb one molecule of CO2 two molecules of MEA are required, producing ion pair of MEACOO- (carbamate) and MEAH+ (protonated MEA). In this research, the MEA thermal degradation is investigated through employing hybrid intelligent techniques of artificial neural network-particle swarm optimization (ANN-PSO) and coupled simulated annealing-least square support vector machine (CSA-LSSVM). The models development is carried out utilizing experimental data, and the input parameters are MEA concentration, CO2 loading, temperature, and time, and the output is the remaining MEA concentration after experiencing the degradation phenome-non. The results can be employed for the further improvement of a solvent-based PCC process in terms of energy efficiency and operation cost. More importantly, the findings of this study can be used for the detailed and more accurate modeling and optimization of the corresponding processes.