<p>This paper conducts a thorough and rigorous analysis of student performance using machine learning algorithms and proposes corresponding strategies for optimizing examinations. The research is structured into three key aspects: first, the application of nonlinear models, such as Random Forest and Gradient Boosting Trees, to identify critical factors influencing student performance and provide targeted learning recommendations; second, the use of clustering analysis to stratify students and assess the difficulty coefficients of every question in the math paper, thereby optimizing exam difficulty; and third, the employment of linear regression models to predict student performance and validate correlations among different mathematics subjects. Additionally, this study incorporates Generative Adversarial Networks (GANs) to enhance model optimization, further improving their generalization capabilities and prediction accuracy. The findings reveal that midterm performance is a pivotal indicator for final grade warnings and that there is a significant correlation within mathematics subjects. Furthermore, exam question difficulty should be aligned with students’ learning levels. These results provide a scientific foundation for enhancing teaching quality, improving student learning efficiency, and offering valuable insights for teachers in implementing personalized teaching and optimizing examinations.</p>