As a result, considerable research effort has been directed towards utilizing computer simulations to determine these thermodynamic properties. In conjunction with thermodynamic integration, molecular dynamics and Monte Carlo techniques have been employed to calculate the free energy for various systems, like ionic hydration and biomolecules. Density Functional Theory(DFT) also provides a framework to predict the thermodynamic properties of materials by considering the electronic structure and interactions within a system. Density Functional Perturbation Theory(DFPT) is an extension of DFT that allows for the calculation of vibrational properties and phonon modes, which are essential for determining the Gibbs free energy at finite temperatures. DFPT and calculate various properties of crystalline materials, such as entropy, Gibbs free energy, and specific heat etc. There are studies, where ab initio Molecular Dynamics has been utilized to estimate free energy. Even though the computational techniques mentioned above provide valuable insight into the estimation of thermochemical properties, they often require significant computational resources and can be time-consuming. In recent years, the substantial data generated through computational and experimental methods, along with the application of advanced machine learning(ML) techniques, has propelled the field of materials science into a new era often described as the fourth paradigm of scientific exploration. Different ML algorithms are utilized to generate efficient and accurate predictions of mechanical and electronic properties of materials. Using solely chemical descriptors, Legrian et al. constructed an ML model capable of predicting thermodynamic properties, such as vibrational entropies and free energies, of crystalline compounds. Bartel et. al utilized the SISSO approach to develop a set of simple and accurate descriptors for predicting the Gibbs energy of stoichiometric inorganic compounds with high resolution across a wide temperature range. These descriptors enable the accurate prediction of reaction energetics for solid-state reactions, offering insights into materials stability, synthesizability, and the temperature-dependent scale of metastability for inorganic compounds. Laiu et al. proposed a data-centric deep learning approach using neural networks to predict the thermodynamic properties of ternary solid solutions. In machine learning, the ability to construct models with minimal data is crucial for applications where data scarcity or highdimensional spaces pose challenges. Physics-informed neural networks(PINNs) offer a promising solution by incorporating domain-specific knowledge and physical principles into the model architecture. By integrating physics-based constraints and relationships, these models can effectively leverage limited data to make accurate predictions and enhance the robustness of the machine learning framework. In this study, we leveraged the Gibbs free energy equation which gives rise to a configuration that enables the model to predict all three thermodynamic properties: free energy, entropy, and total energy simultaneously. This model, termed ThermoLearn (Thermodynamics-learned Neural Network), demonstrates superiority over existing algorithms in different scienarios. The model is benchmarked on two distinctly different datasets-one experimental and the other computational. Notably, both datasets are significantly small (details provided in the Data Extraction section) This attribute is particularly vital in fields such as chemistry and materials science, where data often exist in limited quantities. Furthermore, ThermoLearn demonstrates an exceptional ability to assimilate domain-specific knowledge, which enhances its performance on unseen datasets. To illustrate this, we demonstrated that the model exhibits superior predictive accuracy in scenarios where the test set (out of distribution data) significantly deviates from the distribution characteristics of the training set. Additionally, it has been demonstrated that ThermoLearn performs effectively irrespective of the feature representation techniques employed, such as crystal-compositional features or graph-based features. This robustness is attributed to ThermoLearn's ability to pick up underlying physical principles, enabling accurate predictions across different methods. Last but not least, we demonstrated the model's accuracy by predicting the variation of all thermodynamic quantities as a function of temperature for several materials.