Thermoelectric materials are environmental-friendly energy conversion materials. Their performance optimization is a complex issue of multi-parameter coordination, which becomes a challenge. Although the computational simulation and experimental methods for thermoelectric materials have developed rapidly, the efficiency of searching thermoelectric materials still needs to be further improved. Machine learning has some advantages of low computational cost and high prediction speed, which can shorten the search process and accelerate the corresponding studies on the structure and performance optimization of thermoelectric materials. This review introduced the research progress on machine learning for small sample numerical data (data volume is about 10²), large sample numerical data (data volume >10⁴) and image data in thermoelectric materials from the perspective of data types. Moreover, different machine learning algorithm models used for the structure and performance of thermoelectric materials in different data types were discussed. In addition, the future development and application direction were also prospected.