Machine learning (ML) has emerged as a transformative technique in materials science, revolutionizing the prediction and inverse design of novel materials while significantly reducing experimental costs. Polymers, which underpin critical applications in fields such as medical devices, energy storage, and aerospace engineering, pose unique challenges due to their complex and diverse structures. Accurate prediction of diverse polymer properties, such as glass transition temperature (𝑇 𝑔 ) and band gap (𝐸 𝑔 ), is essential for tailoring polymers to meet specific process regression (GPR) and Bayesian neural network (BNN), adopt a probabilistic modeling approach, using Bayesian inference to derive posterior distributions that inherently capture the uncertainty. Each of these strategies offers distinct mechanisms for UQ, ranging from sampling-based variability to intrinsic probabilistic calculations and model-integrated uncertainty estimation.