This paper leverages data mining to examine the borrowing behavior in an academic library to improve resource management and gain insights into student behavior and performance. Key findings include peak borrowing on Mondays, reduced activity on weekends, and higher demand at semester starts. Mechanical Engineering students are the most active borrowers. An interesting insight from the analysis is that dictionaries are among the most frequently borrowed books by students. A small fraction of items (37.22%) and patrons (27.16%) account for 80% of circulation events, following the Pareto Principle. While machine learning models offer insights into future trends, predictions using this dataset currently have significant limits. Furthermore, linking library data to scholarship data reveals that 8 out of 10 most active students got some scholarships. These findings provide actionable insights to enhance library operations and user satisfaction through personalized strategies and inventory optimization. A recommendation system can be built to provide customized lists of books and materials for students with each distinct profile and academic performance.