The Balanced Academic Curriculum Problem (BACP) is a complex optimization problem in educational institutions, involving the allocation of courses across academic terms while satisfying various constraints. This study aims to optimize BACP using the Teaching-Learning Based Optimization (TLBO) algorithm, addressing the limitations of existing approaches and providing an efficient framework for curriculum balancing. The novelty lies in applying TLBO to BACP, offering a parameter-free, nature-inspired metaheuristic that balances exploration and exploitation effectively. The proposed method models BACP as a mathematical optimization problem and implements TLBO to minimize total load balance delay across academic terms. Computational experiments were conducted on 12 benchmark BACP instances, comparing TLBO against eight other metaheuristic algorithms. Results demonstrate TLBO's superior performance, achieving the best solutions in 75-83% of test problems across various indicators. Statistical analysis using the Wilcoxon rank-sum test confirms the significance of TLBO's improvements. The study concludes that TLBO is a robust and efficient tool for optimizing BACP, outperforming existing methods in solution quality and convergence speed. Future research could focus on enhancing TLBO through hybridization with other algorithms and applying it to real-world BACP scenarios in educational institutions.