This chapter examines course and exam timetabling problems, which remain among the most extensively studied NP-hard optimization challenges, driven by increasingly complex academic regulations, resource limitations, and the rapid transformation of higher education environments. This review systematically examines metaheuristic-based solution methodologies for university course and exam scheduling published between 2020 and 2026. A structured selection strategy was applied to identify studies exclusively focusing on metaheuristic or hybrid metaheuristic techniques such as Genetic Algorithms, Simulated Annealing, Variable Neighborhood Search, Hyperheuristics, Artificial Bee Colony, Whale Optimization Algorithm, and Firefly Algorithm and their applications on real-world and benchmark datasets. Recent literature demonstrates three dominant trends: the increasing prevalence of hybrid metaheuristics that combine global exploration with strong local search operators, significant improvements achieved through multi-neighborhood and hyper-heuristic frameworks, and a gradual shift toward multi-objective and integrated models. Despite promising developments, major research gaps remain, particularly the limited number of integrated course and exam timetabling formulations and the underutilization of machine learning–assisted metaheuristics. This review presents a unified synthesis of methodological advancements, highlights emerging trends, and offers a strategic roadmap for future innovations in metaheuristic timetabling research.