This study conducts a systematic bibliometric analysis of the applications of artificial intelligence (AI) in river management systems from 2000 to 2024. By examining 477 publications retrieved from the Web of Science Core Collection and utilizing CiteSpace for visualization, we identify key research trends, collaborative networks, and emerging themes in this rapidly advancing field. The findings reveal a significant geographical concentration of research output, with China (101 papers), the United States (76 papers), and the United Kingdom (29 papers) ranking as the leading contributors. The analysis highlights an exponential increase in publications, particularly after 2020, with a primary focus on machine learning applications for water quality monitoring and real-time prediction systems. Notable institutions, including the University of Malaya, the Chinese Academy of Sciences, and Duy Tan University, have demonstrated high research productivity. Moreover, critical gaps are identified, such as insufficient stakeholder engagement and the need for more transparent AI model development. These insights offer valuable guidance to environmental managers and policymakers aiming to implement AI-driven solutions for sustainable river management.