It is recognized that chemical datasets are usually smaller and more diverse compared to other fields. Although both the datasets used in this study, are for inorganic materials, they are very different from each other due to inherently different referencing used for collecting the properties. Moreover, the featurization of these datasets differs significantly from each other. Although these datasets may be bunched under the umbrella term of "Free energy" the truth is they are incompatible with each other, which means the hope of combining these datasets for direct use or transfer learning goes out of the window. To further complicate the issue, even within a single dataset, the presence of clustered data points can significantly impair the performance of machine learning models when applied to clusters that differ from those on which they were trained. This phenomenon, known as outof-distribution (OOD) prediction, presents a considerable challenge. In this study, we intend to alleviate these issues of small datasets and robustness against OOD prediction by incorporating laws of physics in the model. One important thing about thermodynamic data is, that irrespective of the simulation method we usually compute the partition function of the system, using which we can access different thermodynamic quantities like Energy, Free Energy, Entropy, etc. One shortcoming of general regression methods is that these require quite a lot of data, since a single target quantity is used. Separate models need to be built to predict different target quantities. Constructing these regression models for each thermodynamic quantity can be labor-intensive due to the need for individualized featurization and algorithm selection. Moreover, building these models for small datasets presents challenges related to overfitting and poor performance in OOD regime. However, ThermoLearn offers several advantages over this approach. Firstly, it is trained on all three thermodynamic quantities (Free energy, total energy, and entropy) simultaneously, streamlining the modeling process. Secondly, the thermodynamic constraint allows us to handle small datasets effectively, hence mitigating the risk of overfitting and poor performance in the OOD regime commonly encountered in such scenarios. Finally, empirical evidence suggests that ThermoLearn exhibits superior performance in terms of accuracy and robustness compared to traditional regression models, making it a promising tool for thermodynamic property prediction. Although this study focuses on Gibbs Free Energy and collects quantities like entropy and total energy as a byproduct of the architecture, one can also build it the other way around.