GPR stands as a paramount non-parametric technique in the domain of ML for providing estimations of uncertainty. By leveraging a defined prior, GPR facilitates the derivation of a prior distribution from labeled data through Bayesian inference, embodying a principled approach to UQ. A critical component of GPR is the kernel function, also known as the covariance function, such as the Radial Basis Function (RBF) or exponential kernel. This function underpins the correlation structure across the variable space, enabling nuanced modeling of data relationships.