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This study presents an AI-based framework that unifies civil and mechanical engineering principles to optimize the structural performance of steel frameworks. Unlike traditional methods that analyze material behavior, load-bearing capacity, and dynamic response separately, the proposed model integrates these factors into a single hybrid feature space combining material properties, geometric descriptors, and load-response characteristics. A deep learning model enhanced with physics-informed reliability constraints is developed to predict both safety states and optimal design configurations. Using AISC steel datasets and experimental records, the framework achieves 99.91% accuracy in distinguishing safe from unsafe designs, with mean absolute errors below 0.05 and percentage errors under 2% for reliability and load-bearing predictions. The system also demonstrates high computational efficiency, achieving inference latency below 3 ms, which supports real-time deployment in design and monitoring environments. the proposed framework provides a scalable, interpretable, and code-compliant approach for optimizing steel structures, advancing data-driven reliability assessment in both civil and mechanical engineering.