Metal additive manufacturing (AM) has the advantages of novel part morphology, new material property, and short production cycle, attracting significant attention in automobile, aerospace, and other fields. Nevertheless, the repeatability and stability of part quality are difficult to guarantee in the current metal AM process, hindering the development of metal AM technology. Vital process ... [Show full abstract] variables such as molten pool characteristics, spatter characteristics, surface morphology, and radiation temperature are directly related to the quality of parts. Monitoring the dynamic changes vital variables is critical. Image processing techniques such as image transformation, recognition, segmentation, and enhancement are introduced to obtain manufacturing process information through processing and analyzing rich image features. The monitoring system’s processing results are usually collected in real time to help solve the repeatability and stability problems of the metal AM process. Presently, in situ and real-time monitoring methods are the most popular, and unfortunately, the literature lacks a comprehensive report. Thus, this paper thoroughly describes in situ and real-time process monitoring on the basis of image processing for metal AM. This paper reviews the in situ and real-time monitoring on the basis of traditional image processing to analyze monitoring objects, process classifications, and image processing carriers. Then, the advantages and disadvantages of in situ and real-time monitoring of metal AM based on artificial intelligence are discussed and compared. Finally, image processing algorithm generalization, quality, small samples, and image labeling problems are analyzed and discussed. A technical route for metal AM real-time feedback control is proposed by combining image processing with other technologies.