Abstract: Additive manufacturing (AM) is a revolutionary breakthrough in the manufacturing of modern high-end equipment. In order to promote the mass production and reliable applications of AM-processed components, the major determining factors include manufacturing repeatability, quality reliability, and performance predictability. However, the combined effects of anisotropic microstructure, randomly distributed defects, internal residual stresses, and surface roughness pose a challenge for the prediction accuracy and efficiency of mechanical properties through traditional empirical models and limited testing data. Recently, as an inevitable product of the development of big data and artificial intelligence to a certain stage, machine learning (ML) has demonstrated a great potential for modelling the complex nonlinear relationships among high-dimensional physical quantities, which has received continuous attention in the field of predicting the mechanical properties of AM-processed materials. This paper offers a comprehensive review of the research progress in predicting the mechanical properties of AM-processed metals and components using ML methods. First, the common ML algorithms (parametric and non-parametric models) and general ML procedures (data preparation, model establishment, and model application and evaluation) are briefly introduced. Special attention is devoted to exploring the characteristics and construction methods of the advanced physics-informed machine learning (PIML), with specific discussions on the physics-informed model input, construction and output. Furthermore, the reasons for the formation of the four major influencing factors on the mechanical properties of AM-processed materials (anisotropic microstructure, manufacturing defects, residual stresses, and surface roughness), and the current application status of ML in predicting these influencing factors are summarized. This paper focuses on the representative research results of ML and PIML in predicting the tensile and fatigue fracture properties of AM-processed metals. Finally, the limitations of ML in predicting the mechanical properties of AM-processed metals, as well as the hot topics and technological prospects, are pointed out.