Abstract: Confronted with global carbon neutrality goals and market volatility, the traditional rigid steel production model must evolve through artificial intelligence (AI)-driven intelligent transformations. This study examines the deep integration of digital and intelligent technologies into steel production, focusing on their applications in critical processes, such as converter steelmaking, electric arc furnace smelting, refining, and continuous casting. It further proposes the integrated use of multimodal sensing and early warning systems, along with resource collaboration and optimization, to enhance the system-wide efficiency and support the transition toward smart and sustainable steelmaking. A key focus is the limitations of existing models and algorithms in effectively handling the multiobjective, multiconstrained nature of steel production. Current approaches remain largely centered on parameter prediction and local optimization for individual processes or equipment, with insufficient emphasis on dynamic coordination between upstream and downstream operations, full-process closed-loop control, and multiobjective tradeoffs. The advancement of cyber-physical production systems in the steelmaking–continuous casting process relies on two foundational elements: precise process control enabled by the synergy between the mechanism and data models, and coordinated operation across multiple processes. Furthermore, by exploring strategies that combine metallurgical domain expertise with large-scale modeling, this study outlines promising directions for future development. Thus, this study provides a roadmap for transforming the experience-driven steel industry into a data-driven, self-optimizing manufacturing paradigm, advancing the sector toward greater intelligence and sustainability.