<![CDATA[The integration of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Generative Adversarial Networks (GANs) into Additive Manufacturing (AM) has opened new horizons for intelligent, efficient, and adaptive production processes. This paper provides a comprehensive review of current trends, diverse applications, and emerging challenges in the convergence of these technologies within AM systems. We explore how AI-driven techniques contribute to real-time monitoring, defect detection, process optimization, and design generation, enhancing the overall quality, precision, and scalability of 3D printing. ML and DL approaches enable predictive modeling and adaptive control, while GANs offer promising capabilities in generative design and synthetic data augmentation. The review highlights key research contributions, technological advancements, and industrial implementations, mapping the landscape of intelligent AM. Moreover, it discusses the limitations of data availability, model interpretability, computational requirements, and integration complexities. Finally, the study identifies future directions for research, including hybrid AI models, physics-informed learning, and sustainable AM development. By synthesizing multidisciplinary insights, this paper aims to guide researchers and practitioners toward more intelligent, automated, and sustainable additive manufacturing frameworks through the strategic adoption of AI and its subfields. Keywords: Additive Manufacturing, Machine Learning, Artificial Intelligence, 3D Printing, Deep Learning]]>
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