Student stress in higher education remains a pervasive problem, yet many institutions lack affordable, scalable, and interpretable tools for its detection and management. The objective of this research is to develop a cost-effective, survey-based stress classification model using multiple machine learning algorithms and eXplainable Artificial Intelligence (XAI) to support transparent and actionable decision-making in educational environments. The analysis revealed five principal predictors: blood pressure, perceived safety, sleep quality, teacher-student relationship, and participation in extracurricular activities. Results demonstrate that both physiological indicators and psychosocial conditions contribute meaningfully to stress prediction. The study concludes that institutional interventions targeting health monitoring, campus safety, behavioral support, relational pedagogy, and extracurricular engagement can effectively mitigate student stress. These findings provide an empirical foundation for the development of integrated policies in higher education aimed at promoting student well-being.
