Global logistics is undergoing a profound transformation as evolving trade demands, supply chain volatility, and technological acceleration converge to challenge traditional operational models. In a landscape defined by increased consumer expectations, tighter delivery cycles, and rising cost pressures, conventional logistics strategies—grounded in static routing and reactive resource allocation—no longer offer the responsiveness or resilience required to maintain global leadership. As emerging economies ramp up their investment in transportation innovation and digital infrastructure, maintaining competitiveness requires a strategic pivot toward intelligence-driven decision-making frameworks. This paper explores the integration of predictive artificial intelligence (AI) models as a foundational approach to redefining logistics leadership. Emphasizing the capacity of machine learning and forecasting systems to anticipate freight flows, model traffic congestion, optimize fleet utilization, and reduce fuel inefficiencies, the research frames predictive AI not as an incremental improvement, but as a transformative enabler. By analyzing systems architectures, key deployment platforms, and case applications in fleet management, the study reveals how predictive AI can unlock new levels of supply chain agility and real-time responsiveness. Particular focus is given to the implications for national logistics strategy, with the United States positioned as a pivotal case. The paper argues that deploying predictive AI at scale—integrated across cloud, edge, and telematics infrastructure—constitutes a critical pathway for strengthening America's competitive advantage in global logistics innovation, while supporting sustainability, efficiency, and long-term resilience.