Magnetic levitation (Maglev) systems are inherently nonlinear and unstable, requiring advanced control strategies to ensure stable and efficient operation. This study presents a comparative analysis of two control approaches: a hybrid robust control strategy based on mixed H₂/H∞ with regional pole placement, and a neural network-based model reference controller. A nonlinear mathematical model of the Maglev system is developed by integrating electromagnetic and mechanical dynamics. The proposed controllers are implemented and evaluated using MATLAB/Simulink under both step and random reference inputs. Key performance indicators such as levitation height, velocity response, and current consumption are analyzed. Simulation results demonstrate that the hybrid H₂/H∞ controller provides improved stability, reduced overshoot, and faster settling time compared to the model reference controller. The findings highlight the effectiveness of robust control techniques in handling system nonlinearities and disturbances in Maglev applications.