Abstract Background Traditional, complementary, and integrative medicine (TCIM) describes a broad collection of medical interventions, practices, and belief systems that fall outside the purview of conventional medicine. Accompanying the recent growth of TCIM research productivity, artificial intelligence (AI) technologies have increasingly impacted areas of biomedical research, including diagnosis, treatment planning, and drug discovery. This bibliometric analysis explores the characteristics of research publications at the intersection of TCIM and AI. Methods A search string encompassing terms related to TCIM and AI was run on MEDLINE on 14 November 2025, with no restrictions by date, language, or publication type. Retrieved MEDLINE records were subsequently used for DOI citation searches in Scopus, with results exported on the same date. The following bibliometric data were collected: number of publications (including total publications and publications per year), open access status, subject area, document type, publication stage, publications per journal, author affiliations; funding sponsors, publication country, source type, and publication language. Trends in the bibliographic were generated in Excel, and bibliometric networks were visualized using VOSviewer, with thematic clusters identified and presented. Results A total of 1917 publications (n = 921 open-access) by 9749 unique authors, were published from 1991 to 2026. The greatest number of publications were published over the last 5 years, with the most productive journals/sources being Scientific Reports (n = 86), PLoS One (n = 63), and IEEE Transactions on Neural Systems and Rehabilitation Engineering (n = 59). The most productive countries included China (n = 982), the United States (n = 353), and the United Kingdom (n = 90), with frequent institutional affiliations and funding sponsors also being from these countries. Conclusions This bibliometric analysis provides insights into research productivity at the intersection of the TCIM and AI, showing rapidly increasing growth in the field. Future work can continue to investigate changes in the publication characteristics of emerging research, as the volume of publications on this topic is expected to rapidly grow.