Artificial Intelligence in Mental Health Nursing: A Bibliometric Analysis of Research Trends, Knowledge Structures and Emerging Themes

Introduction Artificial intelligence technologies are increasingly transforming mental healthcare through predictive analytics, language‐based systems and digital innovations. Mental health nurses play a central role in implementing and evaluating these technologies. Aim/Question This bibliometric review aimed to analyse publication trends, knowledge structures, influential contributions and emerging themes in artificial intelligence research within mental health nursing. Method A comprehensive search of the Scopus database was conducted in May 2026. After eligibility screening following the PRISMA 2020 reporting framework, 141 English‐language articles and reviews published between 2018 and 2026 were included. Bibliometric mapping and keyword co‐occurrence analyses were performed using VOSviewer. Results Publication output increased substantially after 2023, with 72.3% of studies published in 2025–2026. The United States, China and Türkiye were the most productive countries. Five thematic clusters emerged: prediction and risk assessment, nursing education and simulation, nurses' attitudes and workforce impact, generative artificial intelligence applications and ethical‐professional issues in mental health nursing. Discussion Findings indicate a transition from traditional predictive modelling toward implementation‐focused, generative artificial intelligence and ethically informed research. Implications for Practice Mental health nurses require enhanced artificial intelligence literacy, critical appraisal skills and ethical competencies to support safe implementation. Recommendations Future research should evaluate clinical effectiveness, implementation outcomes, cross‐cultural validation, international collaboration and ethical governance of artificial intelligence technologies in mental health nursing.