Proton Exchange Membrane Fuel Cell (PEMFC) systems represent a crucial clean energy component, offering a sustainable alternative to traditional power sources. However, ensuring the dependable and optimal performance of PEMFC systems is essential for their widespread adoption and integration into various applications, ranging from transportation to stationary power generation. To overcome the challenges associated with maintaining the reliability and efficiency of PEMFC systems, there is a growing reliance on Artificial Intelligence (AI)-based diagnosis techniques. AI has emerged as a transformative tool in analyzing complex data patterns, detecting anomalies, and facilitating autonomous decision-making. In the context of PEMFC systems, AI-driven methodologies offer innovative solutions for diagnosing faults, predicting system performance, and optimizing operational parameters. By using AI techniques such as Machine Learning (ML), Neural Networks (NN), expert systems, and data-driven approaches, researchers aim to enhance the reliability, efficiency, and durability of PEMFC systems while minimizing downtime and maintenance costs. The application of AI in PEMFC system diagnosis involves the development of sophisticated algorithms capable of analyzing various data sources, including sensor readings, system parameters, and environmental conditions. These algorithms can detect anomalies, diagnose faults, and provide recommendations for remedial actions in real-time, ensuring continuous and reliable operation of PEMFC systems. While previous papers may have addressed aspects of PEMFC system diagnosis, this comprehensive survey aims to systematically synthesize existing literature to identify shared concepts and research gaps. This extensive survey encompasses a spectrum of AI methodologies, Machine Learning (ML) algorithms, Neural Networks (NN), expert systems, and data-driven approaches associated with diagnosing PEMFC systems. We reviewed over 750 papers to identify shared concepts and real-world implementation issues for future research directions.