Abstract: This review systematically summarizes the current applications, key challenges, and future prospects of machine learning in chemical looping technology. As an efficient pathway for carbon capture and energy conversion, chemical looping relies heavily on the design of oxygen carriers and the optimization of reactor systems. Traditional research methods, which depend on trial-and-error experiments and computational simulations, are often costly and time-consuming. Machine learning, with its data-driven modeling capabilities, demonstrates significant potential in high-throughput screening, reactor optimization, and process control. This paper begins by introducing the fundamental concepts of chemical looping and machine learning, followed by a detailed discussion of recent advances in machine learning-assisted oxygen carrier performance prediction, synthesis optimization, reactor modeling, process simulation, and fault diagnosis. Finally, the review addresses current challenges such as data scarcity, model interpretability issues, and limited generalization across systems, and proposes future directions including physics-informed machine learning, federated learning, and digital twins to accelerate the intelligent and industrial deployment of chemical looping technology.