This study examines the challenge of scheduling in higher education institutions, emphasizing organizational difficulties stemming from increasing student populations and limited resource availability, particularly in terms of physical infrastructure and teaching staff. Through a systematic review of the literature, we explore traditional and advanced methodologies, including heuristic and metaheuristic techniques, as well as the emerging applications of Artificial Intelligence (AI). A bibliometric analysis further examines the adoption of advanced AI techniques in university scheduling. This review identifies trends and limitations in recent studies, proposing a theoretical framework to guide future research. Findings suggest that AI holds substantial potential for automating scheduling tasks, optimizing resource allocation, and reducing manual intervention. However, technical and methodological challenges persist. Future research directions are suggested to develop more sophisticated solutions for addressing the complexities of scheduling in educational contexts. If approached correctly, advanced AI could provide a scalable and customized solution to the scheduling problem.