This study explores the transformative impact of artificial intelligence (AI) in intelligent manufacturing, particularly under the industry 4.0 paradigm. We discuss the integration of AI technologies within manufacturing systems, such as machine learning, computer vision, and natural language processing, leading to enhanced efficiency, flexibility, and customization. The critical applications highlighted include predictive maintenance, quality control, and robotic process automation. This study delves into two specific AI-driven systems: a visual counting system for the food industry and an automatic defect detection system for metal-pressed components. The visual counting system employs deep learning techniques, notably the YOLOv8 algorithm, to accurately count food products and significantly improve inventory management by minimizing errors and waste; similarly, the defect detection system leverages AI to automate the inspection of metal components, reducing reliance on manual inspections, and enhancing production efficiency. While these AI implementations offer substantial benefits, the paper also addresses the challenges of integrating such technologies, including robust infrastructure, data security concerns, and continuous system updates. Despite these challenges, this study concludes that AI’s role in manufacturing is pivotal and will continue to drive innovation and transformation in the industry, paving the way for future advances in smart factories and autonomous production systems.