With the in-depth penetration of information technology in various fields, there are many data in the real world. This can help data-driven algorithms in machine learning obtain valuable knowledge. Meanwhile, high-dimension, excessive redundancy, and strong noise are inherent characteristics of these various and complex data. In order to eliminate redundancy, discover data structure, and improve data quality, prototype learning is developed. By finding a prototype set from the target set, the data in the sample space can be reduced, and then the efficiency and effectiveness of machine learning algorithms can be improved. Its feasibility has been proven in many applications. Thus, the research on prototype learning has been one of the hot and key research topics in the field of machine learning recently. This study mainly introduces the research background and application value of prototype learning. Meanwhile, it also provides an overview of specialties of various related methods in prototype learning, quality evaluation of prototypes, and typical applications. Then, the research progress of prototype learning with respect to supervision mode and model design is presented. In particular, the former involves unsupervision, semi-supervision, and full supervision mode, and the latter compares four kinds of prototype learning methods based on similarity, determinantal point process, data reconstruction, and low-rank approximation, respectively. Finally, this study looks forward to the future development of prototype learning.