As a new class of materials with excellent properties, high-entropy materials (HEMs) have attracted wide interests, in the scientific community owing to their excellent catalytic potential and corrosion resistance. Most traditional studies on high-entropy catalysts are carried out independently, based on existing knowledge systems, which are incompatible and cannot be merged. This has hindered subsequent research and development of better catalysts. Machine learning (ML), as a new strategy to establish mathematical models and conduct research and reasoning based on large data sets, is gradually becoming a branch of artificial intelligence science. The establishment of large databases through ML can effectively transform the traditional research landscape and considerably improve research efficiency. ML can be employed to identify quantitative compositionstructure-performance relationships, providing a novel approach to accelerate the design of electrocatalysts by learning from historical data without explicit programming. This review introduces ML algorithms and HEMs, and it describes and analyses the application of ML in the design of high-entropy electrocatalysts. Finally, the prospects of ML in the screening and prediction of electrocatalysts are discussed and summarised.