This study proposes an artificial intelligence-based automated information management and optimization model, combining genetic algorithm and fuzzy optimization algorithm, aimed at addressing the inefficiencies and communication barriers present in traditional information management systems. By integrating the genetic algorithm and fuzzy optimization algorithm fusion, the model strikes a balance between global search capabilities and local optimization abilities, effectively improving decision accuracy and resource allocation efficiency. Experimental validation demonstrates that the model performs exceptionally well in equipment fault prediction and production scheduling in the manufacturing industry, significantly improving equipment utilization and task execution efficiency. Compared to traditional optimization algorithms, the genetic algorithm and fuzzy optimization algorithm fusion offer clear advantages in convergence speed, computational accuracy, and global search capacity. This study provides an innovative solution for the field of information management, with significant practical implications, especially in smart manufacturing and industrial internet environments, offering broad application prospects.