This chapter introduces unsupervised machine learning (ML) methods and highlights their growing applications in public health research and practice. Unlike supervised approaches, which depend on labeled outcomes, unsupervised methods explore data without predefined categories, uncovering hidden structures, patterns, and relationships within complex datasets. Core techniques such as clustering (e.g., k-means and hierarchical clustering) and dimensionality reduction (e.g., principal component analysis, PCA) are demonstrated step by step using built-in R datasets, providing readers with hands-on experience. These examples illustrate how unsupervised ML can be applied to tasks such as population segmentation, subgroup identification, and exploration of high-dimensional health data. The chapter also discusses key strengths, such as flexibility in discovery, alongside challenges including interpretability, data quality, and computational intensity. By critically examining these opportunities and limitations, readers gain practical and conceptual guidance for the responsible application of unsupervised ML in advancing public health research.