<p>Artificial intelligence has progressively revolutionized across different fields over the years, with particularly notable applications in dam engineering for monitoring and behavior prediction. Anomalies in these structures can lead to critical structural failures affecting stability and dam safety. For this reason, this review presents the contributions of artificial intelligence in dam monitoring for various applications, highlighting that many studies have been conducted on dams in China. A bibliometric study is included in this review to analyze the keywords network and the integration of artificial intelligence in civil engineering and dam monitoring by country, based on the last few years. This research focuses on machine learning models used to monitor deformations, cracks, seepage, and other anomalies encountered in structural health monitoring for dams. For training and testing these models, different methods and technologies are used for data collection, particularly sensors installed in dam structures, drone technology for image input, and the Building Information Modeling (BIM) method to facilitate relations among different participants and enable continuous monitoring of dam behavior. These models continue to develop, offering effective monitoring performance, although they require rich databases for optimal results. Additionally, artificial intelligence algorithms can be used for applications other than prediction and monitoring, such as data denoising and optimization. Multiple models can be combined to achieve comprehensive real-time dam monitoring with optimal results and high accuracy. </p> Graphical abstract <p></p>