The integration of predictive analytics into structural engineering is transforming the way buildings are designed, evaluated, and optimized. Traditional structural design methodologies rely heavily on deterministic calculations, historical assumptions, and predefined safety factors. While these approaches have enabled the development of safe and reliable structures for decades, the increasing availability of large-scale engineering data, sensor networks, computational modeling, and machine learning techniques has introduced new opportunities for performance-driven decision-making. This article explores the role of predictive analytics in modern architectural engineering, focusing on data-driven structural design methodologies, digital engineering workflows, structural health prediction, risk forecasting, and lifecycle performance optimization. The study argues that future structural systems will increasingly be designed using continuously evolving data environments that improve accuracy, efficiency, resilience, and sustainability throughout the asset lifecycle.