Sports injuries are one of the biggest challenges in professional sports that can have a devastating impact on athletes' performance. There are various methods to prevent these injuries, including preventive training, medical care, and the use of new technologies. In this regard, artificial intelligence, especially machine learning algorithms, has been proposed as an innovative solution in data analysis and simulation of complex behaviors of the human body exposed to injury. The aim of this research is to design and implement a predictive model based on neural networks to identify footballers who are at risk of knee and groin injuries. The statistical population of this study includes professional footballers of Nzaja Physical Education in the age groups of Omid and the country's first adult league in the 1403 season. Data was collected through questionnaires, specialized tests, and overlay sensors, and after preprocessing, it was presented to various neural network models for analysis. The performance of the models was evaluated using criteria such as accuracy, features, and F1. The results of data analysis indicate the high capabilities of artificial intelligence in predicting and identifying injuries and hidden patterns. This research can greatly help medical teams and coaches in intelligently preventing injuries and improving training processes.