Solubility is a key property of interest in numerous fields, including oil and gas, biomass conversion, and pharmaceutical science. However, the experimental determination of solubility is often challenging as well as time-consuming. Therefore, developing predictive models for estimating solubility is highly beneficial. Applications of predictive solubility models include screening pharmaceuticals based on their aqueous solubility and selecting suitable solvents for organic molecules of interest. The importance of predicting the solubility of molecules in water (aqueous solubility) has driven the development of many models, starting with simple linear equations like the General Solubility Equation and Estimated SOLubility. Recently, machine learning (ML) models have emerged as the preferred approach for predicting aqueous solubility because of their ability to model highly non-linear functions for solubility predictions. ML models for predicting the solubility of organic molecules in organic solvents (organic solubility) have also been developed, although they are less common than those for aqueous solubility.