Structural optimization has become one of the most transformative developments in civil, architectural, and industrial engineering. Driven by computational design tools, uncertainty modelling, and machine learning techniques, optimization approaches are reshaping how structures are conceptualized, analyzed, and built. This review synthesizes contemporary research on topology optimization, parametric modelling, multi-objective frameworks, uncertainty quantification, and machine-learning-driven strategies for structural design. Key contributions from recent studies highlight how parametric frameworks enable flexible industrial buildings, how multi-material lattices enhance robustness, and how uncertainty-aware optimization strengthens safety and performance. The review also examines holistic workflows linking material selection, conceptual design, structural analysis, and fabrication. Together, these developments demonstrate a shift toward integrated, automated, and performance-driven workflows capable of handling diverse design goals—including sustainability, lightweighting, cost efficiency, and resilience. This paper critically maps the evolution of structural optimization and identifies emerging challenges, including computational complexity, data needs, and integration into real-world design practices.