In recent years， artificial intelligence has been widely applied in the field of materials， and the application of machine learning in molecular beam epitaxy （MBE） has attracted attention. Intelligent recognition and feedback based on in-situ reflection high-energy electron diffraction （RHEED） and related material properties in MBE technology can significantly improve the quality and efficiency of material growth， leading to the realization of intelligent epitaxy of epitaxial films. This article focuses on the application of machine learning in MBE. It first introduces commonly used machine learning algorithm models in MBE， and explains the application of machine learning in optimizing growth conditions and specifically summarizes the research progress on machine learning based on RHEED images for different material systems （semiconductor thin films and quantum structure materials， oxide materials， and two-dimensional materials）. A summary and outlook were provided on the existing problems and future development strategies.