Equitable distribution of physicians across specialties is a significant public health challenge. While previous studies primarily relied on classic statistics models to estimate factors affecting medical students' career choices, this study explores the use of machine learning techniques to predict decisions early in their studies. We evaluated various supervised models, including support vector machines, artificial neural networks, extreme gradient boosting (XGBoost), and CatBoost using data from 399 medical students from medical faculties in Switzerland and France. Ensemble methods outperformed simpler models, with CatBoost achieving a macro AUROC of 76%. Post-hoc interpretability methods revealed key factors influencing predictions, such as motivation to become a surgeon and psychological traits like extraversion. These findings show that machine learning could be used for predicting medical career paths and inform better workforce planning.