This study investigates the performance and stability of Model Reference Adaptive Control (MRAC) systems employing a normalized MIT rule for second-order uncertain plants. The research aims to address the parameter drift and numerical instability issues commonly encountered in traditional MIT rule implementations. A comprehensive MRAC framework was developed incorporating a normalized MIT adaptation algorithm with parameter bounds and control saturation. The system was designed for a second-order plant with unknown parameters and a reference model with desired closed-loop characteristics. MATLAB simulations were conducted over 20 seconds with step and sinusoidal reference inputs, including external disturbances to evaluate robustness. The proposed MRAC system demonstrated effective tracking performance with an Integral Absolute Error (IAE) of 15.48 and Integral Squared Error (ISE) of 12.73. The adaptive parameters converged to stable values without divergence, and the system exhibited robust performance under external disturbances while maintaining bounded control signals. The normalized MIT rule with parameter bounds successfully addresses the stability concerns of traditional MRAC implementations and provides a practical solution for adaptive control of uncertain second-order systems.