Differentiating Artificial Intelligence, Machine Learning, Deep Learning, and Data Mining

The terms Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Data Mining (DM) are frequently used interchangeably across academic, regulatory, and industrial domains, despite having distinct conceptual foundations. This ambiguity hinders methodological consistency, regulatory alignment, and interdisciplinary collaboration. This study conducts a systematic overview of literature, including academic texts and regulatory standards from ISO, IEEE, NIST, and the European Commission, to clarify these terms and their relationships. A thematic analysis supports the synthesize of the existing definitions that structures AI as the broadest domain, encompassing ML and DL, while DM is framed as a process that intersects with but remains distinct from AI. The taxonomy is visualized through comparative matrices and hierarchical diagrams, reflecting differences in emphasis across disciplines. Although primarily conceptual, the study provides a foundation for harmonizing terminology and reducing fragmentation in AI-related research and policy discourse. Future work can focus on empirical validation through real-world case studies to assess its practical applicability and adoption challenges.