Hydrogen fuel cells (HFCs) are increasingly recognized as a vital technology for achieving global sustainability, yet their widespread adoption is hindered by challenges related to safety, reliability, and maintenance. This paper presents a comprehensive review of the application of artificial intelligence (AI) in the monitoring, control, and predictive maintenance of HFCs. Existing methods for HFC monitoring often fail to address the dynamic and complex operational challenges posed by varying conditions such as temperature, pressure, and fuel quality, leading to inefficiencies in fault detection, safety assurance, and operational optimization. An analysis of recent advancements reveals key areas where AI methods, including machine learning (ML) and Internet of Things (IoT) integration, have demonstrated the potential to enhance fault diagnostics, predict remaining useful life (RUL), and ensure real-time health monitoring of HFCs. Literature survey suggests that although several AI and ML models have been developed to swiftly predict some of the safety-related parameters for HFCs, the scarcity of HFC-related accidents and the consequent lack of data poses challenges for real-world development of safety protocols, including risk assessment, incorporating these AI models. Consequently, this review proposes strategies to address these limitations and provides a strategic roadmap for navigating complexities, exploiting opportunities, and collectively advancing AI-enhanced HFC technology. © 2025 The Authors