Research in mobility models for Mobile Ad Hoc Networks (MANETs) focuses on accurately capturing the dynamic movement patterns of mobile nodes to improve the design, testing, and performance of routing and communication protocols. Recent studies propose realistic and context-aware mobility models that consider social behavior, group movement, terrain constraints, and energy limitations. Machine learning and mobility prediction techniques are increasingly employed to forecast node trajectories and adapt routing strategies accordingly. Hybrid models that combine random, deterministic, and trace-based movements are developed to simulate diverse real-world scenarios more effectively. These advancements enable more precise evaluation of MANET performance metrics such as connectivity, throughput, and delay, thereby enhancing the reliability and scalability of protocols in highly dynamic network environments.