In this work, we provide a method, software package, and online tool for the prediction of the solid solubility for moderate temperatures up to 350K without the use of any empirical parameters. Additionally, a more accurate method to predict the temperature dependence of solubility for a broader temperature range even approaching the critical point of the considered solvent is available for ∼100 solvents whose critical temperature and critical density are known. To compensate for the lack of available data, we use machine learning for the fast prediction of several properties, and thermodynamic equations to relate those properties to the solid solubility. The main advantage of this method is that only the molecule identifiers (SMILES or InChI) of the solute and solvent are required to make the predictions.