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Abstract: The increasing integration of renewable energy sources and the rising demand for decentralized energy systems have led to the emergence of microgrids and virtual power plants (VPPs) as key components of modern power systems. Optimal scheduling of these systems is crucial for ensuring efficient energy utilization, cost-effectiveness, and enhanced grid stability. This review explores the methodologies, technologies, and frameworks for optimal scheduling in microgrid-based virtual power plants. It examines the role of advanced optimization techniques, artificial intelligence, and machine learning in addressing the challenges posed by the intermittent nature of renewable energy, load uncertainties, and market dynamics. The review also highlights the importance of coordination between distributed energy resources (DERs), energy storage systems (ESS), and demand-side management (DSM) strategies to achieve optimal performance. Furthermore, it provides a comprehensive analysis of recent studies, comparing their approaches, objectives, and performance metrics. This study aims to serve as a valuable resource for researchers and practitioners working toward the development of more sustainable, resilient, and efficient energy systems.