Abstract: The martensitic transformation temperature is the basis for the application of shape memory alloys (SMAs), and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance. In this work, machine learning (ML) methods were utilized to accelerate the search for shape memory alloys with targeted properties (phase transition temperature). A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data. Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys. The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression (SVR) model. The results show that the machine learning model can obtain target materials more efficiently and pertinently, and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature. On this basis, the relationship between phase transition temperature and material descriptors is analyzed, and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms. This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.