Microgrids are viewed as a distributed power generation system, made up of renewable power resources such as PV, wind, battery storage, shiftable loads, etc., to make a system more sustainable and resilient. But owing to the bi-directional intrinsic properties of renewable energy sources and their intermittent flow, and the randomness of renewable energy demand, energy management under a renewable intermittent system is much more complicated than that in traditional distribution systems. This investigation focuses on reviewing energy management systems based on AI for renewable microgrids, covering forecasting, scheduling, real time control, managing uncertainty and implementation challenges. The paper covers optimization techniques, machine learning, deep learning, reinforcement learning and composite methods. In line with the review, AI can also enhance the accuracy of predictions and operation cost and optimize storage utilization and adaptive control. But there are some obstacles to the path of implementing AI: Data quality, complexity, cyber security risk, the absence of hardware validation and the lack of understanding of how the black box decision making process works. Further work with models and forecasting via physics-driven models, probabilistic forecasting, edge implementation, cyber-resilient control and reproducible benchmark testing should be included in future studies to provide reliable implementation for real- world applications.