Metal Additive Manufacturing (AM) has revolutionized the production of complex metal components by enabling the fabrication of intricate geometries with high precision. This technology's potential can be significantly enhanced by integrating artificial intelligence (AI) methods, particularly Machine Learning (ML), which offers advanced capabilities in establishing complex interrelationships and improving system and product quality control. ML algorithms present a transformative opportunity to address manufacturing challenges, optimize resource consumption, and enhance process efficiency by exploring the intricate linkages between process parameters, material properties, part geometry, microstructural characteristics, and their resultant properties. In metal AM processes such as Directed Energy Deposition (DED) and Laser Powder Bed Fusion (L-PBF), ML applications extend beyond process optimization to include defect detection, in-situ monitoring, and the enhancement of manufacturability and repeatability of components.