Predictive control has become a critical methodology for enhancing the autonomy, adaptability, and precision of mobile robots, particularly in trajectory tracking applications. By anticipating future system behaviors, predictive control algorithms optimize performance while adhering to system constraints, enabling effective operation in dynamic and unpredictable environments. This paper provides a comprehensive review of state-of-the-art predictive control algorithms, including Model Predictive Control (MPC), Reinforcement Learning-based Predictive Control, and Adaptive Predictive Control, analyzing their advantages, limitations, and applicability to robotic trajectory tracking tasks. Key challenges, such as computational inefficiency, sensitivity to disturbances, and scalability to real-time applications, are identified as barriers to wider adoption. To address these issues, prospective research directions are proposed, focusing on integrating artificial intelligence and adaptive strategies to improve computational efficiency and robustness. Future contributions aim to develop hybrid algorithms and validate them experimentally across diverse robotic platforms and terrains, ensuring practical applicability. By advancing predictive control strategies, this research seeks to support the development of robust and adaptable trajectory tracking solutions for wheeled mobile robots, with implications for autonomous vehicles, industrial automation, and military applications.