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• The changing requirements and evaluation methods are summarized for metallic bipolar plate coatings. • Hydrophobicity and improved mechanical properties are increasingly critical for metallic bipolar plates. • Insights into the selection of coating material systems are provided. • The material-structure-properties relationship is discussed as the design principle of coatings. • Machine learning prediction and screening is a high-efficiency design method for high-performance coatings. Metallic bipolar plates (BPPs) manufactured by stamping have great commercialization prospects in proton exchange membrane fuel cells (PEMFCs) owing to their significant advantages of low density, small volume, high mechanical strength, and low cost. The development of coatings on metallic BPPs is essential for its application in PEMFCs. However, both the design and preparation of metallic BPP coatings face great challenges of increasing requirements, low efficiency, insufficient performance, and high cost. In present work, the design, manufacture and evaluation of coatings on metallic BPPs are extensively reviewed and explored. The changing requirements including corrosion resistance, conductivity, hydrophobicity, and mechanical properties for metallic BPP coatings and corresponding evaluation methods are comprehensively discussed. Especially, improved mechanical properties are critical for the emerging precoated metallic BBPs. Numerous coatings previously developed are quantitatively compared to provide guidance for the coating material selection. For highly efficient development of high-performance and low-cost coating, the material-structure-properties relationship of coatings is thoroughly summarized as the coating design principle. Moreover, different design methods including experimental, calculation, and machine learning are respectively in depth explored and compared, suggesting that machine learning combining decreased experiments and material calculation is a high-efficiency and effective method for coating design.