The increasing complexity of modern buildings and infrastructure systems has created unprecedented challenges for engineering management, operational efficiency, lifecycle optimization, and resilience. Traditional approaches to infrastructure monitoring often rely on periodic inspections, fragmented datasets, and reactive decision-making processes that struggle to keep pace with rapidly changing operational conditions. Digital Twin architectures have emerged as a transformative paradigm capable of integrating physical assets, sensor networks, operational data streams, simulation models, and artificial intelligence into unified real-time management ecosystems. This article explores Digital Twin architectures from a systems engineering perspective, examining their role in enabling continuous infrastructure awareness, predictive analytics, lifecycle optimization, and intelligent decision-making across the built environment. Particular attention is given to data integration frameworks, cyberphysical systems, BIM interoperability, AI-driven analytics, predictive maintenance, resilience engineering, and autonomous infrastructure management. The article argues that Digital Twins represent more than digital representations of physical assets; they constitute intelligent operational ecosystems capable of fundamentally transforming how buildings and infrastructure are designed, managed, and optimized throughout their lifecycles.