The rapid advancement of digital technologies has intensified the use of big data in marketing decision making, yet the evolution and research structure of this domain remain fragmented. This study examines through bibliometric analysis, the development and thematic structure of big data research in digital marketing from 2015-2025. The dataset comprises 1.076 documents retrieved from Scopus database and analyzed using performance analysis and science mapping methods to identify publication trends, influential sources, and thematic clusters. The findings reveal a significant increase in publication output, with an average annual growth rate of 8.48%, particularly between 2019 and 2023, aligning with the global acceleration of digital transformation following the pandemic. Keyword network analysis identifies two main thematic clusters: (1) business and marketing dimensions, represented by keywords such as big data, marketing, and commerce; and (2) supporting technologies, including artificial intelligence and the Internet of Things. Geographically, research productivity is dominated by China (likely 363 documents), India (505), and the United States (409). Citation analysis indicates that earlier publications receive higher average citations, suggesting that time positively influences scientific recognition. Overall, this study provides a comprehensive overview of the intellectual landscape of big data research in digital marketing. It contributes by mapping the field’s evolutionary trajectory and identifying future research directions, particularly regarding the integration of advanced technologies and data-driven innovation in digital business practices