This paper describes a new framework to integrate artificial intelligence (AI) with steel structural design for high-risk infrastructure industries such as oil & gas, petrochemical, and refinery usage. Employing machine learning (ML), deep learning (DL), and neural networks (NNs), the framework transforms traditional structural workflows to intelligent, adaptive processes. Trained with large collections of real-world engineering projects, AI models demonstrate significant performance enhancements—reducing design cycle time by 27%, raising structural accuracy, and enhancing resistance to dynamic strain from operational forces. The outcome heralds a new paradigm for industrial engineering, profiling by example how predictive modeling can be employed to design more safely, more efficiently, and code-compliant structures.