In summary, the development of ThermoLearn represents a significant advancement in predictive modeling for thermodynamic properties. Its unique multi-output architecture allows for the simultaneous prediction of key parameters such as Gibbs free energy, total energy, and entropy, providing a comprehensive understanding of material behavior. ThermoLearn outshines existing algorithms in terms of accuracy and robustness across multiple datasets, including experimental and computational ones. Moreover, the architecture of ThermoLearn is distinctively designed to integrate domain-specific knowledge, substantially improving its efficacy on datasets not encountered during training. Its robustness is consistently demonstrated through its stable performance across diverse feature representation techniques, including crystal-compositional and graph-based features. This versatility originates from ThermoLearn's profound grasp of funda-mental physical principles, enabling it to deliver precise predictions across a range of methodologies. The model is very general and can be utilized for various biochemical and material datasets. Furthermore, we believe that ThermoLearn can be extended and generalized to incorporate additional thermodynamic equations, such as the Eyring equation for rate constants and Gibbs free energy. Overall, ThermoLearn presents a promising approach for accurately predicting thermodynamic properties in chemistry for small datasets, offering potential applications in various research domains.