This bibliometric study examines the overall research trends and productivity of Artificial Intelligence in self-directed learning, based on published articles (2005-2025). Initially, we identified 1,801 studies from the Scopus database using the search string ("Artificial Intelligence" OR "AI") AND ("Self-directed Learning" OR "Self-learning"). Finally, we selected 219 articles for analysis after filtering by articles, Conference papers, and reviews within social science, psychology, multidisciplinary, and arts and humanities subjects. Utilizing VOS viewer software, we observed that most studies (38.2%) were in the social science subject area, and China is the dominant contributor, followed by the USA and Germany. Colby College (n = 4 articles) is the leading institution by the number of documents, followed by the University of Oxford and Indiana University Bloomington. Feldmann J., Youngblood N., Wright C.D., Bhaskaran H., and Pernice W. are the most influential authors based on citations. The United Kingdom is the leading country, followed by Germany and South Korea, according to citation counts. The leading source is “Sustainability Switzerland” (n = 7 articles), but the most cited journal in the context of citations is “Nature”. The article “All-optical spiking neurosynaptic networks with self-learning capabilities” by Feldmann et al., (2019) was highly cited (1072). Ogata H., Flanagan B., Majumdar R., Li H., Hwang G.J., Yang Y., Chen X., Liu Y., Zhang J., and Wang Y. are the most frequently co-cited in artificial intelligence research on self-directed learning. In the co-author analysis, the USA is the most collaborative country, followed by the UK and China. “Artificial Intelligence”, “Machine Learning”, “Learning Systems”, “Self-directed Learning”, and “ChatGPT” were the most common co-occurrences among the authors' keywords. The primary theme, based on the author’s keywords, is "Artificial Intelligence and Neural Learning for Sustainable Optimisation". This