Models for predicting the solubility of compounds in sub-and supercritical carbon dioxide were developed using the curated full dataset, both with and without the additional generated thermodynamic features. An important but often overlooked aspect in the literatureevaluation of the model's performance on out-of-training set data -was addressed in this study. The ability of a model to extrapolate to chemical structures that were not encountered during training is rarely assessed in studies where property values depend on external state parameters. This oversight may lead to an inaccurate perception of the model's predictive accuracy. To assess this capability, we employed various validation protocols that tested both interpolation (prediction of solubility for chemicals within the dataset) and extrapolation (prediction for new molecular structures). The reliability and stability of all models presented in this section were evaluated using statistical parameters, including the mean and standard deviation, based on the results of 5CV.