Academic burnout—characterized by emotional exhaustion, cynicism, and reduced study efficacy—is increasingly recognized as a significant threat to the psychological health and educational success of university students. Its prevalence among academic populations has been widely documented, with negative consequences for well-being, learning, and an increased risk of dropping out. Coping strategies are a key element in regulating stress; however, most research has relied on models focused on single variables or simple bivariate correlations, reducing the complexity of the interplay between adaptive and maladaptive strategies. Very few have examined whether coping strategies naturally self-organize into meaningful subgroups and whether such subgroups function as phenotypes predictive of burnout risk. Advances in unsupervised machine learning provide a unique opportunity to address this gap, supporting a data-driven approach to computational phenotyping in student mental health.