Artificial intelligence (AI) and machine learning (ML) have transitioned from lab experiments to critical tools in additive manufacturing (AM), reshaping workflows from design to post-processing. For instance, generative algorithms now produce aerospace brackets with 40% mass reduction using topology-optimized titanium lattices, while ML vision systems like those in EOS’s Monitoring Suite detect porosity defects in laser powder bed fusion with 92% accuracy, cutting scrap rates by 19% (Scime & Beuth, 2018). Yet challenges persist: ML models trained on Ti-6Al-4V struggle to generalize to Inconel 718 due to divergent thermal behaviors, and AI-generated lattice structures often fail fatigue tests despite simulation approval (Rojek et al., 2025). Beyond technical gaps, ethical concerns loom, as training datasets skewed toward aerospace alloys neglect biomedical polymers, risking flawed biocompatibility predictions. This chapter argues that self-optimizing AM systems must integrate domain-specific physics models and human oversight to balance innovation with reliability.