Artificial Intelligence (AI) has emerged as a transformative paradigm in electrical power systems, enabling real-time optimization, predictive control, and intelligent energy distribution. This paper proposes a unified AI-driven framework for power management integrating machine learning, deep reinforcement learning, and edge intelligence across smart grids, microgrids, and distributed energy resources (DERs). The proposed architecture enhances efficiency, resilience, and sustainability by enabling adaptive load balancing, predictive maintenance, and dynamic demand-response control. A comparative analysis demonstrates that AI-based systems can achieve significant improvements in forecasting accuracy, operational cost reduction, and system stability. Recent studies show load forecasting errors can be reduced below 2% using advanced AI models. The paper further explores challenges such as computational overhead, data integrity, and cybersecurity. Finally, future research directions involving federated learning, digital twins, and AI-integrated energy markets are discussed.
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