<p>Estimation of pharmaceutical solubility values under various conditions has been a subject of great interest and is useful for supercritical processing in enhancing solubility values. In this study, we have accurately estimated the solubility of drugs in supercritical solvent (carbon dioxide) under various pressure and temperature levels. Artificial intelligence models (AIMs) including XGBoost, Gradient Boosting, and Random Forest were developed to calculate the solubility of drugs in supercritical CO₂. A comprehensive dataset (1619 points, 58 drugs, with a solubility range of 10⁻⁷–10⁻³ mol fraction) comprising operational conditions (temperature: 308–348.2 K, pressure: 80–400 bar) and physicochemical properties of drugs was collected and considered in model development and optimization. Logarithmic transformation of solubility values was employed to enhance predictive robustness. Comparative analysis of the developed AIMs revealed that the XGBoost and Gradient Boosting models exhibited the most reliable performance. Solubility values were log‑transformed before modeling, and performance metrics (RMSE, MAE, AARD%) were computed on the back‑transformed solubility values. For the optimized XGBoost, the values of R², RMSE, MAE, and AARD% were determined as 0.998, 0.0259, 0.0134, and 4.12% for training; 0.9832, 0.0210, 0.0114, and 3.60% for validation; and 0.9864, 0.0352, 0.0171, and 9.51% for testing, respectively. Furthermore, the obtained R² (training set) for Gradient Boosting and Random Forest are equal to 0.997 and 0.983, respectively. In addition, sensitivity analysis indicated that pressure and temperature were the most influential variables on model performance in solubility prediction.</p>