In the PB approach, PB eq. (or Poisson eq. in the absence of any salt concentration) is solved by defining an interior dielectric constant for the solute and an external dielectric constant for the solvent. The Generalized Born approach also defines internal and external dielectric constants; however, here no electrostatic equation is solved, rather an expression, obtained from the generalization of the Born equation for a single ion, is evaluated. In both PB and GB approaches, the molecular surface area is calculated and in GB, the socalled Born radii is calculated, which can be time-consuming. Also, there are some inherent limitations for continuum models as they neglect the molecular nature of water. There have been several attempts to build cluster-continuum models. From an entirely different perspective, several machine learning (ML) models are developed, in the last couple of years, to predict solvation free energy using experimental data in the FreeSolv database. The faster speed of ML models compared to physics-based models is advantageous and can be used for large databases of small molecules used in drug discoveries. However, ML models often suffer from a lack of interpretability and the reasons why they work (or do not work) are often not clear. There have been attempts to define descriptors having clear physical meaning. For instance, Zhang et al. used electron density (obtained from quantum mechanical calculations) based descriptors. In some of the other representative works, Alibakhshi et al. have combined ML models with PCM model to predict solvation free energy in different solvents using the components of the PCM calculations as the features of the ML model, Pattnaik et al. have developed an ML model to predict relative solvation free energy in forty-one solvents. Vyboishchikov has developed a few NN-based models based on the GB model of solvation. The effective Born radii, charges are used as the features in these models. Machine learning has also been used to predict the HFE obtained from MD simulation-based methods. In the current work, our motivation is to use a minimal number of physically interpretable and simple descriptors for predicting HFE. Starting from the GB expression and with an approximate analytical calculation of Born radii, we evaluate the HFE (after adding five more descriptors) and got accuracy almost as good as the paper by Zhang et al. for the FreeSolv dataset. The power of our method is that it is completely physics-based, hence fully interpretable. It has only six descriptors, an electrostatics term (GB term summed with Coulomb electrostatic), polar surface area, number of donor and acceptor atoms, logP , and the number of rotatable bonds. We have used four different models, namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), Gradient Boosting (GradBoost), and Light Gradient Boosting Machine (LightGBM). Our best result is a mean absolute error (MAE) of 0.74 kcal/mol comparable to the work of Zhang et al. This method can be used for large datasets used in drug designing and the reason for specific HFE values can be understood clearly as opposed to most of the ML models.