Removing CO2 as an acidic-potential component from different gaseous flows is a main topic in different industries producing green-house gases, especially in natural gas sweetening units. A group of well-known absorbents for CO2 are the amine solutions. The common amine compounds consisting di-ethanolamine (DEA), methyl-di-ethanolamine (MDEA), and their mixture in aqueous solution have been investigated in this study. The effort was to develop new models for estimation of CO2 loading capacity of the presented amine solutions using genetic programing (GP) and stochastic gradient boosting (SGB) trees as two advanced and novel machine learning approaches in this area. A total of 175 sets of experimental data of CO2 absorption including independent variables (temperature, CO2 partial pressure, concentrations of DEA and MDEA in water) and objective function (CO2 loading capacity) were collected from literature and fed to the mentioned algorithms (GP and SGB) as input dataset. Then, each algorithm was run over the dataset, separately and two new models were created. Finally, strict statistical evaluations were implemented to assess the estimating capability of the new models. The statistical parameters including correlation coefficients (R-2 (SGB) = 0.99848 and R-2 (GP) = 0.99087), root-mean-square deviations (RMSDSGB = 0.00903 mol/mol and RMSDGP = 0.02244 mol/mol) and average absolute relative deviations (AARD(SGB) = 0.95628% and AARD(GP) = 8.71909%) show that the utilized powerful algorithms have enhanced the applicability of the new developed models providing good` estimations in operational processes. Final results show superiority and more accuracy of the new SGB model for confident predictions in amine process.