The selection of a suitable solvent is a critical challenge in post-combustion carbon dioxide (CO2) capture processes. This study investigates the effectiveness of potassium sarcosine (KSar) as a novel amino acid salt in both KSar + H2O and KSar + MDEA + H2O systems. The kinetics of CO2 absorption into KSar (3-15 wt.%) + MDEA (20 wt.%) + H2O solutions were examined using a pressure decay technique in a stirred VLE cell at temperatures ranging from 303.15 to 333.15 K. Additionally, the physicochemical properties and mass transfer characteristics were assessed and compared with conventional amine solutions, particularly MDEA (30 wt.%) + H2O. Properties such as density, viscosity, Henry’s law constant, and liquid-phase mass transfer coefficient were applied to analyze the CO2 absorption kinetics. The overall reaction rate constant was determined through both the zwitterion and termolecular mechanisms in the pseudo-first-order reaction regime. The calculated rate constants showed good agreement with the experimental data, with average absolute relative deviations (AARD) of 4.53% and 3.03% for the termolecular and zwitterion models, respectively. The experimental results clearly demonstrated that adding a small amount of KSar to the MDEA + H2O solution significantly improves the CO2 absorption rate. Furthermore, the solubility of CO2 in KSar + H2O and KSar + MDEA + H2O systems was measured in the temperature range of 303.15 to 333.15 K and partial CO2 pressures between 2 and 27 kPa. The experimental solubility data were analyzed using several models, including semi-empirical models, the Deshmukh-Mather model, and artificial neural networks. Performance evaluation based on AARD showed that the artificial neural network model exhibited superior performance in predicting CO2 solubility for both KSar + H2O and KSar + MDEA + H2O solutions. Finally, a rate-based model for post-combustion CO2 capture using KSar + H2O and KSar + MDEA + H2O solutions in a packed column was develo