Thermodynamics, machine learning, and known correlations are combined to build a predictive tool for the solid solubility of neutral solutes in many organic solvents over a wide temperature range. This is the first modeling tool that can predict the solid solubility for a broad range of solvents and temperatures without the necessity for empirical parameters.