This chapter builds on the broad overview introduced in Chap. 1 by focusing on the foundational concepts of artificial intelligence (AI) and machine learning (ML) in public health. It clarifies the distinctions and hierarchical relationships among AI, ML, deep learning (DL), and large language models (LLMs), while introducing supervised, unsupervised, semi-supervised, and reinforcement learning approaches. Using electronic health records (EHRs) as a central example, the chapter illustrates how ML methods can analyze diverse clinical, behavioral, and environmental data streams to generate actionable insights. It also addresses challenges of data quality, interoperability, and algorithmic bias, alongside the ethical safeguards, workforce development, and organizational capacity necessary for sustainable and responsible adoption of AI/ML in public health practice.