This paper presents the development and implementation of a Model Predictive Control (MPC) framework for trajectory tracking in autonomous vehicles under diverse driving conditions. The proposed approach incorporates a modular architecture that integrates state estimation, vehicle dynamics modeling, and optimization to ensure real-time performance. The state-space equations are formulated in a Linear Parameter Varying (LPV) form, and a curvature-based tuning method is introduced to optimize weight matrices for varying trajectories. The MPC framework is implemented using the Robot Operating System (ROS) for parallel execution of state estimation and control optimization, ensuring scalability and minimal latency. Extensive simulations and real-time experiments were conducted on multiple predefined trajectories, demonstrating high accuracy with minimal cross-track and orientation errors, even under aggressive maneuvers and high-speed conditions. The results highlight the robustness and adaptability of the proposed system, achieving seamless alignment between simulated and real-world performance. This work lays the foundation for dynamic weight tuning and integration into cooperative autonomous navigation systems, paving the way for enhanced safety and efficiency in autonomous driving applications.