Machine learning (ML) is accelerating the advancement of additive manufacturing (AM) into an intelligent, autonomous technology, spanning basic process optimization to specialized biomedical implant fabrication. Based on a systematic literature review encompassing 103 screened studies from 2014 to 2025 in accordance with PRISMA-P guidelines, this research maps how ML augments AM workflows in design, in-situ monitoring, and material performance. Supervised techniques like support vector machines, artificial neural networks, and convolutional neural networks are used extensively to forecast melt pool behavior, identify defects, and optimize parameters. Analysis of comparative studies within the reviewed cohort shows that while traditional data-driven models are foundational, physics-informed neural network (PINN) architectures pro-vide a 10–12% increase in microstructural and thermal history prediction accuracy by embedding explicit physical conservation laws relative to purely empirical black-box configurations. Emerging frameworks, including large-scale generative models and federated learning, are evaluated not as immediate clinical solutions, but as experimental methodologies that facilitate advanced inverse design, biomimetic lattice synthesis, and decentralized collaborative manufacturing protocols. Ti-based alloys, particularly Ti–6Al–4V, lead the clinical domain due to structural performance, fine-tuned through ML for site-specific characteristics. The findings conclude that transitioning from 'black-box' models to explainable AI represents the definitive path forward for meeting FDA/CE regulatory standards in clinical validation.