Uncertainty quantification and prediction calibration form the foundation of trustworthy machine learning — the difference between a model that predicts "90% confident" and one where 90% confidence actually means 90% accuracy. These techniques span Bayesian approximations (Monte Carlo dropout, variational inference, Laplace), ensemble-based approaches (deep ensembles, SWAG), post-hoc calibration methods (temperature scaling, Platt scaling), conformal prediction for distribution-free guarantees, and metrics like ECE and Brier score that quantify calibration quality. Two fundamental types of uncertainty drive this field: epistemic uncertainty from model ignorance (reducible with more data or better architectures) and aleatoric uncertainty from irreducible data noise. Whether deploying safety-critical medical AI, building production recommenders that know when to abstain, or quantifying prediction intervals for regression, these methods bridge the gap between raw model outputs and interpretable, actionable confidence scores — a crucial step toward AI systems humans can trust.