Carbon dioxide (CO2) considerably contributes to the greenhouse effects and consequently, to the global warming. Thus, reduction of CO2 emissions/concentration in the atmosphere is an important goal for various industrial and environmental sectors. In this research work, we study CO2 capture by its absorption in mixtures of water and piperazine (PZ). Experimental techniques to obtain the equilibrium data are usually costly and time consuming. Thermodynamic modeling by Equations of State (E0Ss) and connectionist tools leads to more reliable and accurate results, compared to the empirical models and analytical modeling strategies. This research work utilizes Genetic Programming (GP) and Genetic Algorithm Adaptive Neuro Fuzzy Inference System (GA-ANFIS) to estimate the solubility of CO2 in mixtures of water and piperazine (PZ). In both methods, the input parameters are temperature, partial pressure of CO2, and concentration of PZ in the solution. A total number of 390 data points is collected from the literature and used to develop GP and GA-ANFIS models. Assessing the models by the statistical methods, both models are found to acceptably predict the CO2 solubility in water/PZ mixtures. However, the GP exhibits a superior performance, compared to GA-ANFIS; the values of Average Absolute Relative Error (AARD) are 5.3213% and 9.7143% for the GP and GA-ANFIS models, respectively. Such reliable predictive tools can assist engineers and researchers to effectively determine the key thermodynamic properties (e.g., solubility, vapor pressure, and compressibility factor) which are central to design and operation of the carbon capture processes in a variety of chemical plants such as power plants and refineries. (C) 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.