<p>Accurate estimation of the solubility of solid drugs (SDs) in the supercritical carbon dioxide (SC-CO₂) plays an essential role in the related technologies. In this study, artificial intelligence models (AIMs) by gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS) methods were applied to estimate the solubility of SDs in SC-CO₂. Hence, a comprehensive database (1816 datasets) comprising operational conditions (<i>T</i>, <i>P</i>) in the wide ranges (308–348.2&#xa0;K and 80–400&#xa0;bar), SD’s molecular weight (<i>MW</i>_<i>SDs</i>), and melting point (<i>MP</i>_<i>SDs</i>) were gathered. Investigation analysis of the models’ strength showed that the model developed by ANFIS exhibited a more satisfactory approximation than the GEP model. According to the optimized ANFIS model, statistical parameters of R², RMSE, MAE, and AARD% were obtained, equivalent to 0.991, 0.260, 0.167, and 13.890% for training and 0.990, 0.256, 0.157, and 15.273% for validation, in that order. Sensitivity analysis showed that the highest effect of independent variables on calculating SDs solubility in SC-CO₂ belong to <i>MW</i>_<i>SD</i>s, P, <i>MP</i>_<i>SDs</i>, and T, respectively. Therefore, <i>MW</i>_<i>SD</i>s is a key factor for modeling the solubility of various SDs in SC-CO₂. Comparing the estimated results obtained from the optimized AIM with previous semi-empirical models showed that the AIMs could be more accurate in modeling the solubility of SDs in SC-CO₂.</p>