Hydrogen energy is gaining prominence as a clean, green, and efficient energy source, leading to increased research in hydrogen–electric coupled microgrid systems. These systems aim to enhance energy reliability and efficiency, especially in the presence of renewable energy sources like wind and solar power. Global scholars have conducted in-depth research to analyze conversion mechanisms and coupling relationships between various energy forms, using technologies like fuel cells and electrolytic hydrogen production systems. Literature reviews have explored modeling, operational control methods, dispatch strategies, and optimization algorithms to improve the performance of hydrogen–electric coupled microgrids. While precise modeling optimization has shown limitations, advancements in deep reinforcement learning methods offer new insights for energy management in these systems. Strategies based on deep reinforcement learning, Q-learning algorithms, and neural networks have been proposed to optimize energy operations, minimize costs, and improve the economy of integrated energy systems. Despite challenges in decision-making actions and computational complexity, the integration of hydrogen energy in microgrids demonstrates significant potential in addressing energy challenges and promoting sustainability.