The ensemble and BNN models not only demonstrated superior accuracy but also maintained a favorable correlation between predicted uncertainties and actual errors. However, the ensemble method's computational intensity, due to multiple training iterations, may limit its applicability for larger datasets. On the other hand, BNN offers a balanced profile with its ability to adjust uncertainty scales through predefined weight distributions, showcasing its potential for extensive use in polymer's 𝑇 ! prediction. GPR, despite its accurate uncertainty scaling, faces challenges in aligning errors with uncertainties, as indicated by the negative correlation in Spearman's analysis.