Objective With the expansion of theories concerning aging and the rapid evolution of artificial intelligence (AI) methodologies, the integration of AI and longevity medicine has emerged as a critical pathway for extending human healthspan. This study is aimed at a systematic characterization of the research landscape, collaboration networks, and frontier trends in this interdisciplinary field, so as to inform clinical translation and the development of the health management industry.

Methods The Web of Science Core Collection, including Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI), was used as the data source. English-language publications in the interdisciplinary field of AI and longevity medicine between 2010 and 2025 were systematically retrieved, and 4170 valid records were included for a joint analysis using Bibliometrix and CiteSpace 7.0.

Results Annual publication output showed exponential growth. The global research landscape exhibited a China–US dual-core and a multipolar pattern, with institutional collaborations clustering into 3 major networks spanning Asia–Europe, the United States, and Continental Europe. Research topics were clustered into five major themes—the core concepts of aging and multimorbidity, AI-driven body composition and musculoskeletal assessment, deep-learning-based evaluation of brain aging through neuroimaging, wearable sensing and mobile health, and digital phenotyping and mental health. Marked differences in research priorities were observed among the three leading contributors, including China, the United States, and the United Kingdom. China demonstrated broad research coverage and clinically oriented multi-track development, the United States led in methodological innovation and digital phenotyping, and the United Kingdom concentrated on brain age research.

Conclusions This study systematically delineates the research landscape, collaboration networks, and frontier trajectories in the interdisciplinary field of AI and longevity medicine, providing data-driven evidence and decision-making support for advancing clinical translation and the development of the health management industry in this field.