Student mental health and academic stress are significant concerns in higher education, creating a need for effective approaches to early risk identification. This study proposes a multiclass predictive framework for academic stress and mental health risk classification among students. The framework was developed using a Kaggle dataset containing 25,000 records with demographic, academic, behavioral, psychological, lifestyle, and medical attributes. A unified comparison was conducted across six conventional machine learning algorithms, namely Decision Tree, Random Forest, XGBoost, CatBoost, K-Nearest Neighbors (KNN), and Naïve Bayes, and three deep learning architectures, namely Convolution... More >