As next-generation information technologies have quickly changed, generative AI has become a key player in the creation and growth of digital industrial clusters. Generative AI strengthens the importance of computing power, power resources and data elements, gives rise to a new mode of virtual agglomeration, and changes the formation mechanism of digital industrial clusters. At the same time, generative AI broadens the range of innovation actors, enhances the flow of knowledge, facilitates dynamic coupling between technological innovation and application-layer scenario innovation,thereby boosting the endogenous innovation vitality of digital industrial clusters.Generative AI also speeds up the restructuring of value chains based on big models, pushes the reorganization of organizational boundaries, and builds open cluster collaboration networks. These have jointly driven the systematic transformation of digital industrial clusters. Currently, while facing key development opportunities such as market growth, policy incentives, technological breakthroughs, and a chance for global cooperation. However, it also confronts challenges including core technological bottlenecks,industrial ecosystem coordination dilemmas,slow-moving governance regulations, and inadequate human capital fit.Thus, policy suggestions are made: strengthening research and development in important technologies, setting up multi-tiered coordination systems for the industrial ecosystem, improving governance standards and ethical frameworks for data elements and algorithmic security, and creating a system for developing a variety of skills.These offer pragmatic direction for enhancing generative AI’s facilitation of superior development within digital industrial clusters.