The objectification of facial diagnosis has been developed through recent years and has become a multidisciplinary research topic.However,many studies are still limited to the adjustment of algorithms and the design of data collection environment and equipments,few studies focus on facial diagnosis zoning.The purpose of this paper is to discuss the problems in the current research literature on machine learning-based intelligent TCM facial diagnosis zoning to build a foundation for subsequent related research.The study uses bibliometric methods and text analysis to clarify and analyze the current intelligent TCM facial diagnosis zoning methods,which mainly include facial feature point-based,facial feature block-based and complete face-based method;then by analyzing the influencing factors of facial diagnosis zoning research and summarizing the common machine learning algorithms,the advantages and disadvantages of different machine learning algorithms and the corresponding common facial diagnosis zoning methods are obtained;Finally,we discuss three aspects of the current phase of facial diagnosis zoning research:dataset construction,advantages of deep learning,and embodiment of facial diagnosis theory.