Due to industrial development, the volume of carbon dioxide (CO₂) is rapidly increasing. Several techniques have been used to eliminate CO₂ from the output gas mixtures. One of these methods is CO₂ capturing by ionic liquids (ILs). Computational models for estimating the CO₂ solubility in ILS is of utmost importance. In this research, a white box model in the form of a mathematical correlation using the largest data bank in literature is presented by the group method of data handling (GMDH). This research investigates the application of GMDH intelligent method as a powerful computational approach for predicting CO₂ solubility in different ionic liquids with temperature lower and upper than 324 K. In this regard, 4726 data points including the solubility of CO₂ in 60 ILs were used for model development Moreover, seven different ionic liquids were selected to perform the external test. To evaluate the validity and efficiency of the suggested model, regression analysis was implemented on the actual and estimated target values. As a result, a proper fit between the experimental and predicted data was obtained and presented by various figures and statistical parameters. It is also worth noting that the predicted negative values in the proposed models are considered zero. Also, the results of the established correlation were compared to other proposed models exist in the literature of ionic liquids. The terminal form of the models suggested by GMDH approach and obtained based on temperature are two simple mathematical correlations by exerting input parameters of temperature (T), pressure (P), critical temperature (T_c), critical pressure (P_c) and, acentric factor (ω) which does not suffer from the black box property of other neural network algorithms. The model suggested in this work, would be a promising one which can act as an efficient predictor for CO₂ solubility estimation in ILs and is capable of being used in different industries.