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This paper reviews recent advancements in fog computing and deep reinforcement learning for smart grid demand response systems. It analyzes key developments in fog architectures, learning techniques, and energy optimization for distributed energy management. With the rise of IoT devices and renewable energy, traditional cloud-based systems face challenges such as high latency, limited scalability, and energy inefficiency. Through analysis of recent literature, we highlight major gaps, including the lack of integrated fog-reinforcement learning frameworks, limited adaptability to real-time demand fluctuations, and the absence of holistic solutions addressing multiple performance issues simultaneously. While current methods show improvements in specific areas (e.g., 15–35% energy savings or 47% latency reduction), they lack integrated frameworks to deliver comprehensive, real-time optimization for future smart grids. This review provides a systematic framework for developing integrated approaches that address these complexities, offering actionable insights for real-world smart grid deployment.