The insights establish a study for selecting reliable ML models for polymer informatics, facilitating advancements in polymer discovery and design. Figure (a) highlights the comparative performance of six ML models using R 2 , calibration area and Spearman's rank correlation coefficient as evaluation metrics. For dataset 𝑇 𝑔 𝐸𝑋𝑃 , GPR achieved the highest R 2 value (0.831), as shown in Figure .1, reflecting its superior accuracy in predicting the mean. However, GPR failed to establish a relationship between predicted uncertainties and actual errors, as indicated by its near-zero Spearman's coefficient. While its calibration was the best among all models, the lack of alignment between errors and uncertainties reduces its practical utility in functional polymer discovery, where confidence in predictions is paramount. BNN, despite having a slightly lower R 2 (0.756), outperformed all other models in UQ metrics, achieving a high Spearman's coefficient value (ρ=0.217) and maintaining competitive calibration. This balance between accuracy and reliable UQ makes BNN the most suitable model for guiding experimental validation, particularly in scenarios where prioritizing high-confidence predictions is critical. The findings for 𝑇 𝑔 𝐸𝑋𝑃 dataset highlight the performance of BNN in identifying promising polymer candidates for experimental testing. Its ability to provide reliable uncertainty estimates ensures that high-confidence predictions align with low errors, reducing the risk of resource-intensive experimental validation of less viable candidates.