Under the backdrop of the profound restructuring of the global manufacturing competitive landscape and the emergence of a new wave of technological revolution, AI-enabled digital transformation in manufacturing has become a Strategic Initiatives for breaking through industrial upgrading bottlenecks and cultivating new quality productive forces. Through systematic literature review and analysis of global trends in AI-driven manufacturing transformation, this study reveals the evolutionary patterns, global competitive dynamics, and future challenges of AI-empowered digital transformation in manufacturing. Key findings include: (1) AI technology penetration exhibits phased leapfrogging characteristics, evolving from single-point applications and partial process optimization to systemic empowerment. This transformation is driven by a three-dimensional mechanism—technology infiltration, process reengineering, and ecosystem reconstruction—that comprehensively accelerates manufacturing digitalization. (2) The global landscape demonstrates five salient features: data elements becoming strategic assets, systemic enhancement of full-chain efficiency, structural transformation of corporate operational logic, accelerated strategic deployment by major economies, and the emergence of AI governance as a global common agenda. China has developed a distinctive pathway leveraging its "technology-market-industry" synergistic advantages. (3) Three-tier challenges persist: technical barriers including data silos and algorithmic black boxes, industrial constraints involving green transition pressures and standardization gaps, and geopolitical tensions over technological dominance and fragmented governance frameworks. The study proposes future research priorities: innovation in AI foundational logic and its impact on reshaping global manufacturing geography, pathways for AI-enabled green-digital synergy in sustainable manufacturing, and strategies to navigate international AI competition toward an open, win-win global community. This research provides a systematic framework for understanding the strategic value, implementation pathways, and global competition patterns of AI-driven manufacturing digitalization, offering policy implications for developing new quality productive forces and advancing China's manufacturing powerhouse construction.