The Predict-Update Cycle is a fundamental mechanism in Bayesian filtering and state estimation, where predictions about a system's state are continuously refined using new observations. This cycle enhances the accuracy of models by iteratively applying prediction based on prior knowledge and updating with incoming data, thus minimizing uncertainty over time.