Supervised learning techniques, including classification and regression, are used for fault diagnosis and risk scoring, while unsupervised learning methods such as clustering and dimensionality reduction support pattern discovery in process data. Semi-supervised and self-supervised approaches leverage unlabeled industrial data, and probabilistic machine learning with uncertainty quantification is critical for safety-focused decision-making.