We developed a machine learning model that can provide fast and accurate predictions of kinetic solvent effects for a wide range of neutral reactions and solvents. A large set of training data were generated using the COSMO-RS method for over 28,000 reactions and 295 solvents. The performance of the model was evaluated with both calculated and experimental data using rigorous data splits. The model achieves the MAEs of 0.71 and 1.03 kcal/mol on unseen reactions for the prediction of solvation free energy and solvation enthalpy of activation (∆∆G ‡ solv , ∆∆H ‡ solv ), respectively, relative to the COSMO-RS calculations. The model is shown to provide reliable predictions of relative rate constants when tested on the experimental set that contains unseen reactions with much bigger molecules than those found in the training set. We also demonstrate that different types of data sets can be effectively used via a transfer learning approach to refine the predictions.