This paper introduces a novel approach to enhance the magnetic levitation system's robustness by integrating a Fractional Order Proportional-Integral-Derivative (FOPID) controller tuned using a nature-inspired algorithm. The utilization of nature-inspired algorithms for controller tuning is motivated by their ability to mimic the efficient and adaptive characteristics observed in natural systems. The proposed controller design leverages the FOPID structure, which introduces fractional calculus concepts to the classical PID controller, allowing for a more flexible representation of system dynamics. Two different nature-inspired algorithms have been employed to optimize the parameters of the FOPID controller. Teaching–Learning-Based Optimization (TLBO) and African Vulture Optimization Algorithm (AVOA) have demonstrated success in solving complex optimization problems. These algorithms are well-suited for controller tuning as they explore the solution space efficiently and effectively, enabling the identification of optimal controller parameters that enhance system performance. The suggested approach has been validated through thorough simulations of a magnetic levitation system. The results demonstrate the superiority of the African Vulture Optimization Algorithm (AVOA) algorithm-based FOPID controller's robustness, disturbance rejection, and transient response compared to its counterpart. The adaptive nature of the controller allows it to adapt to varying operating conditions and uncertainties, making it well-suited for real-world applications where system dynamics may change over time.