The transition toward more sustainable renewable energy sources has driven advancements in energy storage technology, including the development of Battery Energy Storage Systems (BESS). To improve the reliability and efficiency of BESS, implementing an effective monitoring system is essential, especially for detecting and diagnosing battery faults. The most commonly utilized methodologies for the diagnosis of faults in battery systems encompass knowledge-based, model-based, and data-based approaches. Artificial Intelligence (AI) holds significant potential to enhance fault diagnosis systems through predictive models capable of analyzing large datasets, identifying patterns, and forecasting potential faults. This work offers a thorough investigation of AI applications for BESS fault diagnosis, supported by an in-depth review of reliable sources such as Science Direct, IEEE Xplore, and Scopus. A total of 723 papers from scientific publications over the last 5 years were initially considered in this research. Following a rigorous screening process, including duplicate removal and the application of exclusion and inclusion criteria, 28 studies were selected for quantitative analysis. This study not only examines the types of faults that can be diagnosed but also assesses the challenges associated with recent advancements in this technology. In this context, the research identifies several aspects that have been applied within the theory of AI-based fault diagnosis for BESS and offers recommendations for further research. The results of this study are intended to aid in the creation of fault diagnosis systems that are more dependable and effective, which in turn will support the transition to cleaner and more sustainable energy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.