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 through the integration of 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, microstructural characteristics, and their resultant properties. This thesis aims to determine the most precise ML algorithm for achieving the process parameters defect detection relationship of AI316L stainless steel alloy components containing 2.5% copper fabricated via the Laser Powder Bed Fusion (L-PBF) method.