This paper examines the critical role of data-driven project management in optimizing multinational supply chains. In an increasingly complex global market, businesses face the challenge of managing cross-border operations efficiently while maintaining flexibility and responsiveness. The study highlights key project management frameworks, including PMI’s PMBOK, Agile, and Lean methodologies, which provide structured approaches for managing supply chain projects across diverse regions. Furthermore, it emphasizes the integration of advanced data-driven tools, such as IoT, ERP systems, and SCM software, to enhance real-time decision-making, improve operational efficiency, and optimize resource allocation. The research also delves into the significant role of big data analytics, predictive analytics, and machine learning in improving forecasting, identifying inefficiencies, and mitigating risks within supply chain management. Performance tracking using key performance indicators (KPIs) is identified as a pivotal strategy for monitoring project progress and ensuring continuous improvement through feedback loops. The study provides practical recommendations for integrating data-driven tools into project management practices and emphasizes the importance of fostering a data-centric culture within organizations to improve supply chain performance. The paper suggests areas for future research, including the application of artificial intelligence, machine learning, and blockchain technology in enhancing supply chain transparency, risk management, and sustainability. Ultimately, this study offers insights into the evolving landscape of multinational supply chain management, demonstrating how data-driven project management practices can enable businesses to achieve operational excellence, mitigate risks, and stay competitive in a rapidly changing global environment.