A general understanding of solubility prediction was gained with the general solubility equation, which includes solute-solvent interactions through parameters such as activity coefficients, and enables predictions under various conditions. Hildebrand and Hansen solubility parameters describe the properties of solvents and solutes. Hansen developed this idea by introducing three solubility parameters that take into account hydrogen bonding, polarity, and dispersion forces, on which the different properties of the compound depend. In COSMOS-RS quantum chemistry tools are used in the form of a conductor-like screening model for real solvents to forecast solubilities in complex systems. It takes into account the environment, electrostatic interactions, and molecular structure. Recently, significant progress has been made in the solubility prediction field, with a focus on the use of statistical methods supported by rigorous data analysis to uncover hidden patterns and correlations within solubility data. Statistical models leverage advanced algorithms to identify key factors influencing solubility, thereby improving predictive accuracy and machine learning methods . These data-driven approaches harness the power of artificial intelligence to process vast datasets, learn complex relationships, and make accurate predictions.