In this research, a Model Predictive Control (MPC) approach has been utilized to perform trajectory tracking and evaluate vehicle stability within a Formula Student application. The results show that the framework achieves accurate path following while respecting vehicle constraints, offering a reliable and adaptable tool for both simulation and real-world testing. This research has been developed with the purpose of supporting a Formula Student team, aiming to improve vehicle performance through the implementation of advanced control strategies grounded in vehicle dynamics. The central objective is to enhance the drivability of the car, achieve smoother dynamic behaviour, and strengthen the correlation between real-world data and simulation tools by incorporating a deeper understanding of the vehicle’s behaviour. The work focuses on the design and implementation of a Model Predictive Control (MPC) system for trajectory tracking, using the nonlinear bicycle model as the foundation. The system is discretized through Euler integration, enabling real-time feasibility. The MPC framework relies on a quadratic cost function, where weighting matrices are applied to balance the importance of states and control inputs, ensuring optimal trade-offs between accuracy and control effort. Linear inequality constraints are formulated to represent the physical and safety limits of the car, maintaining realistic and safe operating conditions. A structured programming approach in Python was adopted, following object-oriented principles to ensure scalability, modularity, and straightforward integration into the team’s existing environment. The program architecture was designed to allow rapid modification of parameters, weights, and constraints, enabling the team to perform efficient tuning and faster iterations during development and testing. This flexibility not only reduces the time required to evaluate different control setups but also provides a practical foundation for real-world implementation. Overall, the research establishes a robust platform that combines theoretical control concepts with practical usability, supporting ongoing improvements in Formula Student competition performance.