As an advanced intelligent manufacturing technology, additive manufacturing (AM) can directly produce metallic components with complex macroscopic structure in short lead time and has attracted lots of attention in recent years. It has been widely used in many advanced manufacture fields such as aerospace, medical device, and so on. However, there exhibit limited number of alloy systems suitable for AM printing, and the complex printing process makes it easy to introduce defects. Consequently, the large-scale application of AM is hindered. Machine learning has been widely used in various daily life and industrial production fields due to its excellent data processing and analysis capabilities. In this paper, the application of machine learning in the AM process including processing window establishing, printing quality control, printed metallic microstructure identification, and mechanical properties exploration are reviewed. In the end, the opportunities and challenges of machine learning in AM are discussed, and the further research directions are proposed.