The rapid growth of electric mobility has intensified the demand for high-efficiency motor drives that can maximize energy utilization, extend battery life, and improve overall vehicle performance. Traditional motor control strategies, such as Field-Oriented Control (FOC) and Direct Torque Control (DTC), often face limitations in balancing dynamic performance, efficiency, and constraint handling, particularly under fast-changing operating conditions. This research investigates the application of Model Predictive Control (MPC) for high-efficiency electric motor drives, offering a systematic approach to real-time optimization of torque, current, and switching behavior. A comprehensive mathematical model of the motor-inverter system is developed, incorporating constraints on voltage, current, and switching frequency. The proposed MPC framework employs a finite control set to predict future system states and select optimal control actions that minimize a multi-objective cost function encompassing torque ripple, energy loss, and thermal stress. Simulation studies demonstrate significant improvements in efficiency, torque tracking, and dynamic response compared to conventional control methods. Furthermore, hardware-in-the-loop validation confirms the practical feasibility of MPC implementation for real-time electric vehicle applications. The findings indicate that MPC not only enhances the operational efficiency of electric drives but also supports the integration of advanced power electronics technologies, including wide bandgap semiconductor devices, thereby contributing to the next generation of high-performance electric mobility solutions