Hemophilia A is a genetical bleeding disorder characterized by the shortage of Factor VIII, with varying degrees of severity. In this study, we trained several machine learning models, including Logistic Regression, SVM, AdaBoost, and so on, to predict the severity of Hemophilia A (mild, moderate, severe). The datasets used for model training and evaluation are contained clinical and genetic features. As hemophilia is a rare case and very difficult to identify, we have to work with a small set of data. After analyzing various models, we identified AdaBoost demonstrated superior performance, achieving 100% accuracy in predicting severity levels. The results highlight the potential of machine learning techniques to assist in early and precise classification of Hemophilia A severity, aiding in personalized patient care. This research contributes to the increase of work in medical AI and offers an advanced solution for clinical applications.