Abstract: Artificial Intelligence (AI) is revolutionizing modern production through the adoption of data-driven decision-making, predictive maintenance, autonomous systems, and high-precision quality control. This work presents an enhanced and method-centric analysis that restructures the landscape of AI in industrial settings through a new conceptual taxonomy, a systematic review based on learning paradigms, and a cross-mapping of AI approaches with industrial settings. The study includes real industry case studies to demonstrate how AI is implemented in reality. The study analyses key challenges such as data imbalance, interoperability gaps, explainability, real-time limits, and workforce skill limitations. Overall, this review presents a comprehensive and up-to-date perspective that improves knowledge of how AI methods progress manufacturing while highlighting the technical and operational constraints that must be addressed in order to develop reliable, scalable, and autonomous industrial systems.

Keywords: Artificial intelligence, Advanced manufacturing, Technological advancement.

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