Contemporary steel manufacturing is undergoing a strategic transformation through Industry 4.0 adoption, leveraging artificial intelligence (AI), automated systems, and advanced analytics to achieve dual objectives of operational efficiency and environmental sustainability. This study presents an AI-integrated vocational education and reskilling framework specifically designed for steel production environments, synchronizing workforce development with technological innovation. Developed using operational data from an active direct-reduction facility, this framework integrates two complementary components: machine learning (ML) algorithms optimizing metallization parameters and predictive maintenance protocols, paired with an adaptive training curriculum utilizing digital twin simulations and generative AI for skill personalization. Result outcomes demonstrate that AI-driven training significantly boosts workforce skills, while ML improves metallization efficiency and reduces gas consumption. The work highlights the value of aligning vocational education and smart manufacturing infrastructure, revealing measurable improvements in both production metrics and workforce agility. The demonstrated approach provides a replicable blueprint for industrial upskilling, positioning AI-curriculum integration as a strategic imperative for maintaining sector competitiveness while advancing circular production paradigms.