Advancing Student Performance Prediction: A Comparative Analysis of Machine Learning, Deep Neural, and Graph Neural Networks. This research conducts a comprehensive evaluation of predictive methodologies for academic outcomes, extending beyond conventional machine learning (ML) techniques. We systematically assess deep neural networks (DNNs) and graph neural networks (GNNs) for modeling multifaceted student data—integrating demographic and academic attributes—to enhance performance forecasting. Using publicly accessible educational datasets, our quantitative framework employs Python-based tools (scikit-learn, PyTorch) to benchmark established ML algorithms (including decision trees, support vector machines, and gradient boosting) against DNN and GNN architectures. Results demonstrate that DNNs and particularly GNNs significantly outperform traditional models in predictive accuracy and generalization capability. GNNs prove especially effective by capturing complex relational patterns within student data that conventional approaches overlook. This investigation establishes the critical advantage of graph-based representations in educational data mining, providing researchers, educators, and policymakers with advanced methodologies for predicting academic achievement. The study further identifies promising directions for future work, including hybrid modeling techniques and real-time intervention systems for personalized education.