Machine learning (ML) has emerged as a transformative technique in materials science, revolutionizing the prediction and inverse design of novel materials to significantly reduce experimental costs. Polymers, which underpin many important applications ranging from medical devices to aerospace engineering, are notoriously challenging to characterize due to their complex structures. A key polymer property, glass transition temperature (𝑇 ! ), is instrumental in dictating a polymer's phase and mechanical properties across practical applications. The glass transition refers to the smooth and reversible process in amorphous materials where they change from a hard and somewhat brittle state, known as "glassy", to a more viscous or rubbery condition when heated. This transition occurs over a specific range of temperatures, defined by the glasstransition temperature 𝑇 ! of the material. Traditional methods for determining 𝑇 ! , such as differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA), though precise, are time-consuming and resource-intensive. In contrast, ML offers a rapid and cost-effective means for the accurate prediction of 𝑇 ! , underscoring its capability to streamline the development of new polymeric materials with tailored properties. Uncertainty quantification (UQ) is a critical consideration in ML for material property prediction and inverse design for novel materials. For instance, incorporating UQ bolsters the confidence of chemical space for polymer discovery and facilitates the development of uncertainty-based acquisition functions for navigating the chemical space in polymer informatics. Uncertainty in data-driven research is broadly classified into two categories, each stemming from distinct origins.