In recent years, microgrid (MG) deployment has significantly increased, utilizing various technologies. MGs are essential for integrating distributed generation into electric power systems. These systems’ economic dispatch (ED) aims to minimize generation costs within a specific time interval while meeting power generation constraints. By employing ED in electric MGs, the utilization of distributed energy resources becomes more flexible, enhancing energy system efficiency. Additionally, it enables the anticipation and proper utilization of operational limitations and encourages the active involvement of prosumers in the electricity market. However, implementing controllers and algorithms for optimizing ED requires the independent handling of constraints. Numerous algorithms and solutions have been proposed for the ED of MGs. These contributions suggest utilizing techniques such as particle swarm optimization (PSO), mixed-integer linear programming (MILP), CPLEX, and MATLAB. This paper presents an investigation of the use of model predictive control (MPC) as an optimal management tool for MGs. MPC has proven effective in ED by allowing the prediction of environmental or dynamic models within the system. This study aims to review MGs’ management strategies, specifically focusing on MPC techniques. It analyzes how MPC has been applied to optimize ED while considering MGs’ unique characteristics and requirements. This review aims to enhance the understanding of MPC’s role in efficient MG management, guiding future research and applications in this field.