In the materials science community, the reliability of experimental datasets often surpasses that of computed data. Therefore, we chose the JANAF dataset to conduct an in-depth comparative analysis between existing regression algorithms and Ther-moLearn. We use 4-fold cross-validation to compute the performance of the model on the full dataset. These results are shown in Table . Algorithms such as AdaBoost, and Ridge have exhibited subpar performance in predictive modeling tasks. On the other hand, gradient boosting regression(GBR), random forest regression(RFR), extreme gradient boosting(XGB), and neural network regression algorithms have demonstrated comparatively favorable results. Notably, in all the algorithms, the hyperparameters are tuned to maximize the coefficient of determination (R 2 ) score. However, ThermoLearn demonstrates an outstanding 43% reduction in Root Mean Square Error (RMSE) compared to the next best model, showcasing its significant enhancement in predictive accuracy. Additionally, we have benchmarked the performance of ThermoLearn for the PhonoDb dataset against the above-discussed algorithms. As seen with the JANAF dataset, ThermoLearn once again outperforms the rest as seen in table .