Abstract: This study aims to systematically analyze the research progress of machine learning in the field of psychological intervention over the past decade, revealing its development trajectory and frontier directions to provide a reference for subsequent studies. By searching the Web of Science (WOS) Core Collection database for relevant literature published from January 2015 to April 2025, and employing CiteSpace 6.3 R1 software to construct knowledge graphs and conduct keyword and co-cited literature analyses, a total of 303 valid articles were identified. The annual number of publications shows a year-by-year increasing trend. Among them, meta-analysis (betweenness centrality 0.21), depression (betweenness centrality 0.17), and machine learning (betweenness centrality 0.15) constitute the key core nodes in this field. Research on machine learning in areas such as special populations, precision medicine, predictive models, clinical psychology settings, and digital interventions has become relatively mature, suggesting that the digital transformation of psychological interventions may become a future research trend. This indicates that the application of machine learning in psychological intervention has begun to take shape, and future research could further deepen the integration of artificial intelligence and digital technologies to enhance the efficacy of psychological interventions.