Recently, artificial intelligence (AI) has increasingly been integrated into self-regulated learning (SRL), presenting novel pathways to support SRL. While AI-SRL research has experienced rapid growth, there remains a significant gap in understanding the intersection between AI and SRL, resulting in oversight when identifying critical areas necessitating additional research or practical attention. Building upon a well-established framework, from Chatti and colleagues, this systematic mapping review identified 84 studies through the Web of Science, Scopus, IEEE Xplore, ACM Digital, EBSCOHost, Google Scholar, and Open Alex, to explore the intersection of AI and SRL within the four key aspects—Who (stakeholders), What (theory), How (methods), and Why (objectives). The main results revealed that AI-SRL research predominantly focuses on higher education students, with minimal attention to primary education and educators. AI is primarily implemented as an intervention—through adaptive systems and personalization, prediction and profiling, intelligent tutoring systems, and assessment and evaluation—to support students' SRL and learning processes. The direct impact of AI on SRL was primarily focused on the metacognitive and cognitive aspects of SRL, while the motivational aspect of SRL remains underexplored. While over one-third of the AI-SRL studies did not specify an SRL theory, Zimmerman’s model of SRL was the most frequently applied among those that did. The use of AI in supporting SRL has extended beyond just focusing on and supporting SRL itself; it has also aimed to enhance various educational and learning activities as end outcomes such as improving academic performance, motivation and emotions, engagement, and collaborative learning. The results of this study extend our understanding of the effective application of AI in supporting SRL and optimizing educational outcomes. Suggestions for further research and practice are provided. © The Author(s) 2025.