The solubility of four established anticancer drugs, including busulfan, decitabine, temozolomide, and tamoxifen, has already been documented in supercritical CO2 (scCO2) to develop advanced formulations. In the current research, their supercritical solubility was theoretically evaluated by some empirical models, such as PR and SRK (cubic equations of state), UNIQUAC and modified Wilson’s models (expanded liquid models), and an artificial intelligent paradigm. The critical properties of these drugs were calculated using approved group contribution methods. Among the empirical models, the Belghait model provided the best fit to the experimental data, yielding an AARD% value within the range of 5.45–9.37 and an approximate Radj value of 0.990. Comparing SRK and PR models, the SRK model exhibited higher accuracy in correlating solubility data of all the mentioned drugs, achieving mean AARD% and Radj values in the range of 9.32–15.37 and 0.98–0.996, respectively. The UNIQUAC model outperformed the modified Wilson model in accurately correlating the solubility data for all the specified drugs, achieving mean AARD% values of 5.04–6.48 and Radj values in the range of 0.981 to 0.991. Moreover, the artificial neural network model offered the highest accuracy in predicting the solubility of the desired drugs. Notably, approximately 99.7% of its predicted data can fit with the experimental data, indicating its extremely good performance.