University life often brings with it a host of new challenges and transitions that affect students in different ways. While some students adjust well, others may find the academic and social environment overwhelming. This study investigates student patterns by examining their psychological well-being, personality traits, and academic outcomes. Using a combination of cluster analysis and association rule mining, we identified different groups of students based on these factors. The findings suggest that understanding student diversity through a data-driven lens can help institutions develop more effective support mechanisms.