In this study, a bibliometric analysis was conducted on the literature surrounding wind power and machine learning to gain insight into the current state of the field and the research trends and patterns within it. A dataset of research papers on “wind power prediction” and “machine learning” was analyzed using the software tool R- Biblioshiny library for Bibliometrix and VOSviewer. This analysis included the creation of time, collaboration and connection maps, which allowed for the identification of key players and trends in the literature on wind power prediction and machine learning, as well as active areas of research and collaboration. The findings showed that wind power prediction is a rapidly growing and internationally collaborative field, with a particular focus on statistical modeling and machine learning techniques. In addition, the most active universities and countries in the field were identified, as well as the most influential papers based on the number of citations. This bibliometric analysis provides valuable insights into the state of the field of wind power and machine learning and can inform future research and development efforts in this area.