This study systematically evaluated six ML models-Ensemble, MCD, GPR, MVE, BNN and EDL-for predicting key polymer properties (𝑇 𝑔 , 𝐸 𝑔 , 𝑇 𝑚 , 𝑇 𝑑 ) and quantifying uncertainties. Using three independent metrics-accuracy (R 2 ), Spearman's rank correlation coefficient and calibration area-the performance of these models was analyzed by four datasets for different properties, OOD datasets, high-𝑇 𝑔 polymers and diverse polymer types within 𝑇 𝑔 dataset. This comprehensive evaluation provides critical insights into model reliability for UQ in ML. This study underscores the importance of integrating UQ into ML models to prioritize highconfidence predictions and optimize experimental validation efforts. While Ensemble is the best choice for extreme polymer discovery, BNN provides a balanced solution for scenarios requiring both reliability and efficiency, particularly in active learning and iterative design workflows. The primary limitation of this work lies in the challenges posed by simulation-derived datasets, where discrepancies between simulated and experimental data affect model performance. Future research should focus on improving simulation accuracy and exploring hybrid ML frameworks that integrate experimental (high-fidelity) and computational (low-fidelity) data. Expanding the application of UQ to polymer exploration and incorporating experimental validation will further enhance the practical utility of ML models for accelerating polymer discovery.