<p>The global transition toward smart grids and sustainable energy infrastructures demands advanced energy management systems (EMS) capable of integrating intelligent, secure, and adaptive technologies. This review provides a focused synthesis of recent progress in EMS, emphasizing the convergence of AI-driven optimization, IoT-enabled monitoring, blockchain-based energy trading, and renewable-energy integration. Unlike existing surveys that examine EMS technologies in isolation, this work systematically compares quantitative results from recent studies, identifying measurable gains in energy efficiency, cost reduction, and system reliability across diverse applications. It also critically evaluates persisting limitations—such as interoperability gaps, computational complexity, cybersecurity vulnerabilities, and scalability constraints—linking them to empirical findings. Furthermore, the paper outlines emerging paradigms, including quantum-inspired optimization, federated learning frameworks, and decentralized control architectures, which define the next phase of EMS evolution. This comprehensive assessment bridges technological, economic, and policy perspectives, offering a clear roadmap for researchers and practitioners working toward next-generation, data-driven, and sustainable EMS architectures. </p>