Depression among university students has emerged as a significant mental health concern worldwide. Traditional assessment methods primarily rely on self-reported questionnaires and clinical evaluations, which may not provide scalable and continuous monitoring. Recent advances in machine learning have created opportunities to identify depression-related behavioral patterns through digital activity data. However, many predictive models operate as black-box systems that provide limited interpretability. This study presents a comparative analysis of explainable artificial intelligence (XAI) approaches for depression risk assessment using digital behavior data collected from university students. Logistic Regression and Random Forest classifiers were developed using behavioral indicators including screen time duration, social media usage frequency, nighttime device usage, sleep patterns, self-perceived digital dependency, and perceived academic impact. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. SHapley Additive exPlanations (SHAP) were applied to improve transparency and interpretability. Experimental results indicate that Logistic Regression achieved 94.74% accuracy, while Random Forest achieved 100% accuracy on the testing dataset. SHAP analysis identified academic impact as the most influential predictor of depression risk. The findings demonstrate that explainable machine learning models can support transparent and ethical depression risk assessment in higher education environments.