Data scarcity for the solid solubility in different solvents and at different temperatures has limited the use of machine learning for the direct prediction of this property. A software package and an user-friendly web interface are designed and publicly available for the prediction dissolution enthalpy can be neglected. In this case, machine learning models are used for the prediction of ∆H solv,298K and solute parameters E, S, A, B. Together with log(S X,298K ) and the published correlation for ∆H sub,298K , the temperature dependent solid solubility can be calculated. In case the model for S aq,298K is used for the calculation, log(S X,T ) can be predicted with a RMSE/MAE of 1.49/0.99. If experimental data for the solid solubility of the solute in at least one solvent are available, the RMSE/MAE of log(S X,T ) improve to 0.44/0.29. At temperatures higher than 350K, the temperature dependence of the dissolution enthalpy has to be accounted for. This requires the calculation of the saturated solvent density and information on the critical properties of the solvent. If those are available, our method can calculate the solid solubility of solvent/solute pairs at elevated temperatures even approaching the critical point of the solvent.