The integration of Artificial Intelligence (AI) in manufacturing is a transformation towards increased operational efficiency, adaptability, and sustainability in response to the evolving industrial landscape of Industry 4.0. This study explores the multifaceted role of AI in optimizing manufacturing processes, focusing on its implementation in assembly line configurations, equipment selection, and worker management. We investigate the capabilities of AI to adapt assembly lines to fluctuating product designs and market demands, enhance equipment lifecycle management through predictive maintenance, and optimize workforce allocation based on skills and production needs. Our approach combines a systematic literature review with practical case studies to illustrate current applications and potential advancements AI brings to modern manufacturing. Based on our review of research and current industrial practices, we introduce a novel concept termed AI matchmaking. This tool automates the matching process between available equipment in the market and the specific requirements of factory owners. Our discussions with industry professionals underscore the considerable potential of this tool in facilitating the decision-making processes related to equipment procurement and task allocation. Our work also underscores the need for an integrated approach that considers economic, environmental, and societal sustainability dimensions. This paper highlights not only the technological advancements but also addresses the challenges of data integration, scalability of AI solutions, and workforce adaptation in the context of an increasingly automated industrial environment. Through detailed analysis and discussion, we provide a comprehensive overview of AI’s transformative impact on workers and equipment management in assembly lines. We outline potential avenues for future research in which AI can substantially enhance assembly line management. These include developing predictive models to forecast product evolution, optimizing equipment replacement strategies to incorporate second-hand resources, and ethically integrating AI technologies into manufacturing processes, among others.