Injuries in professional football represent a critical issue that significantly affects both team performance and the financial sustainability of clubs. This thesis investigates whether, based on data concerning players’ on-field performance and historical injury records, it is possible to develop an effective predictive model to anticipate the risk of injury in the weeks following a match. The work begins with a structured review of the existing scientific literature on injury prediction in sports, with particular focus on machine learning applications in football. Subsequently, a dataset was constructed by integrating information from publicly available sources (Fbref and Transfermarkt), encompassing detailed match statistics and injury timelines.