The AI-powered workforce optimization model for MNCs’ intricate supply chain ecosystems is the main emphasis of this study. Through the use of machine learning algorithms and data analytics, the model makes it easier to make better decisions, make better use of resources, and run things better in a variety of organizational and geographic domains. The model generates predictive insights and prescriptive suggestions for workforce management by integrating real-time data from many sources, such as production schedules, inventory levels, and market demand projections. MNCs can swiftly adjust to shifting supply chain conditions because to the model’s dynamic scheduling, adaptive learning capabilities, and scenario-based simulations. Unstructured data is analyzed using natural language processing (NLP), and optimization tactics are continuously improved via the use of reinforcement learning. The proposed model achieved 92% reaction time, 84% scalability, 81% optimization efficiency, and 82% accuracy. This strategy allows for high service levels at the lowest feasible costs, the optimal distribution of human resources, and the reduction of operational bottlenecks. It addresses the problem with a strong and marketable design by addressing the complexities of global supply chains, which are frequently hindered by sector-specific nuances.