Abstract: As an advanced method of formation, additive manufacturing greatly promoted the application in the aerospace engineering. Therefore, alloy composition development and process control which are suitable for additive manufacturing are now mainly based on experiences. This restricts the further development of high temperature alloy in additive manufacturing technology. As one of artificial intelligence technology, machine learning improved the traditional trial-and-error material research methods and plays a part which can’t be ignored in the field such as alloy design and performance prediction. This thesis gives a brief introduction to the technical features and applications of additive manufactured superalloys, and then introduces fundamental machine learning methodologies and model classifications. Concrete explanations are detailed given on the applications of machine learning in powder preparation, printing process control and performance prediction. At last, it summarizes the findings and discusses future research and development of machine learning technologies based on additive manufacturing.