Optimization is a critical approach in decision-making processes aimed at enhancing both safety and operational efficiency in industrial systems. This research focuses on the implementation of the Linear Quadratic Regulator (LQR) and Linear Quadratic Tracking (LQT) control strategies to optimize the FL57BL02 DC motor control system, widely used in industrial automation, particularly in high-risk applications such as conveyors and robotic systems. The LQR method is employed to enhance system stability by minimizing output deviations and ensuring optimal control performance under varying load conditions. Meanwhile, LQT is utilized to improve trajectory tracking accuracy, ensuring that the system follows the desired reference with minimal error. Through comprehensive simulation and experimental validation, this study demonstrates that the integration of LQR and LQT control strategies reduces output deviation and transient response errors by up to 25% compared to conventional PID-based controllers. Furthermore, the implementation of these advanced control techniques contributes significantly to Occupational Safety and Health (OHS) compliance by mitigating mechanical vibrations and reducing noise levels both of which are crucial risk factors in industrial environments. By stabilizing system performance, this research presents a novel engineering solution that enhances machine reliability, minimizes downtime, and mitigates the potential for workplace hazards. This study offers an important contribution to the field of automatic control systems by demonstrating how advanced optimal control strategies can be leveraged to enhance industrial safety, improve energy efficiency, and pave the way for the development of more sophisticated OHS-compliant automation technologies in future industrial applications.