In this work, we have developed a physics-based and interpretable machine learning model for predicting hydration free energy of small molecules with only six descriptors. Our results compare well with other works with this dataset. However, the advantage of our method is that the results are fully interpretable, which is often the issue with the ML models. Our models perform well across different chemical groups signifying their applicability to larger databases such as those used in drug discoveries.