Model Predictive Control (MPC) has gained attention in power electronics due to its ability to handle multivariable systems, constraints, and nonlinearities effectively. Despite its high computational demand, MPC is widely used in applications such as active front ends (AFE), power converters, uninterruptible power supplies, and high-performance drives. This review discusses the application of MPC in power electronics, highlighting its advantages and challenges. MPC has evolved significantly in modern control theory and is now being applied in power electronics and drives. The use of MPC in this field is supported by accurate mathematical models and powerful microprocessors that can perform the required calculations quickly. Research from 2007 to 2012 shows that MPC is applied in four main areas: grid-connected converters, inverters with RL output load, inverters with LC filters, and high-performance drives. These applications have attracted significant research attention, with a growing trend in research output. The MPC control strategy involves using a system model to predict future behavior and optimize a cost function to determine control actions. The FCS-MPC method is particularly effective for power converters, considering their discrete nature. This method evaluates possible switching states to find the optimal control action. The FCS-MPC algorithm involves predicting system behavior, evaluating the cost function, and selecting the optimal switching state. MPC is applied in grid-connected converters, active front ends, and active filters. For grid-connected converters, MPC helps regulate the dc-link voltage and inject reactive power. In active front ends, MPC is used for direct power control (DPC) and predictive DPC (P-DPC) with SVM modulation. In active filters, MPC is used to control the output voltage and current, compensating for unbalanced and harmonic currents. MPC is also applied in inverters with RL load and multilevel inverters. For matrix converters, FCS-MPC is used to control input reactive power and output current. In multilevel inverters, MPC is used to balance floating dc voltages and reduce switching losses. The FCS-MPC method is effective for multilevel inverters, considering the balance of floating voltages and reducing switching losses. MPC is used in inverters with output LC filters to achieve sinusoidal output voltage with low harmonic content. The FCS-MPC method is applied to control the output capacitor voltage and current. In high-performance drives, MPC is used for predictive torque control (PTC), which tracks torque and stator flux. The PTC method uses a cost function to minimize tracking errors and select optimal switching states. MPC faces challenges such as accurate system modeling, high computational burden, and harmonic distortion. However, recent advancements in microprocessor technology and modeling techniques have improved the feasibility of MPC in power electronics. Future research focuses on developing analytical tools to evaluate MPC performance without extensive simulations. Overall, MPC is a promising technique for achieving high-performance operation in power electronics and drives.Model Predictive Control (MPC) has gained attention in power electronics due to its ability to handle multivariable systems, constraints, and nonlinearities effectively. Despite its high computational demand, MPC is widely used in applications such as active front ends (AFE), power converters, uninterruptible power supplies, and high-performance drives. This review discusses the application of MPC in power electronics, highlighting its advantages and challenges. MPC has evolved significantly in modern control theory and is now being applied in power electronics and drives. The use of MPC in this field is supported by accurate mathematical models and powerful microprocessors that can perform the required calculations quickly. Research from 2007 to 2012 shows that MPC is applied in four main areas: grid-connected converters, inverters with RL output load, inverters with LC filters, and high-performance drives. These applications have attracted significant research attention, with a growing trend in research output. The MPC control strategy involves using a system model to predict future behavior and optimize a cost function to determine control actions. The FCS-MPC method is particularly effective for power converters, considering their discrete nature. This method evaluates possible switching states to find the optimal control action. The FCS-MPC algorithm involves predicting system behavior, evaluating the cost function, and selecting the optimal switching state. MPC is applied in grid-connected converters, active front ends, and active filters. For grid-connected converters, MPC helps regulate the dc-link voltage and inject reactive power. In active front ends, MPC is used for direct power control (DPC) and predictive DPC (P-DPC) with SVM modulation. In active filters, MPC is used to control the output voltage and current, compensating for unbalanced and harmonic currents. MPC is also applied in inverters with RL load and multilevel inverters. For matrix converters, FCS-MPC is used to control input reactive power and output current. In multilevel inverters, MPC is used to balance floating dc voltages and reduce switching losses. The FCS-MPC method is effective for multilevel inverters, considering the balance of floating voltages and reducing switching losses. MPC is used in inverters with output LC filters to achieve sinusoidal output voltage with low harmonic content. The FCS-MPC method is applied to control the output capacitor voltage and current. In high-performance drives, MPC is used for predictive torque control (PTC), which tracks torque and stator flux. The PTC method uses a cost function to minimize tracking errors and select optimal switching states. MPC faces challenges such as accurate system modeling, high computational burden, and harmonic distortion. However, recent advancements in microprocessor technology and modeling techniques have improved the feasibility of MPC in power electronics. Future research focuses on developing analytical tools to evaluate MPC performance without extensive simulations. Overall, MPC is a promising technique for achieving high-performance operation in power electronics and drives.