This comprehensive review explores the convergence of Digital Twins (DTs) and the Internet of Things (IoT), detailing fundamentals, classifications, architectural strategies, data integration, communication protocols, and analytical applications. The authors trace DT origins from NASA’s Apollo simulations to Michael Grieves’ formalisation in 2002, outlining four maturity stages—from Pre‑DT to fully Intelligent DTs—anchored in real‑time data exchange and machine learning-enabled decision-making . The paper surveys state-of-the-art implementations across domains such as smart cities, autonomous vehicles, and energy-efficient buildings, emphasising use cases like Wellington’s virtual city platform developed with Unreal Engine for net-zero energy optimization. It then evaluates architectural considerations—including connectivity, data pipelines, and modeling—alongside analytical insights, offering a clear taxonomy of DT–IoT integration. Finally, it critically discusses key challenges (e.g., standardisation, security, scalability) and identifies future research directions such as federated learning, plug‑and‑play DT components, and adaptive, intelligent twin ecosystems. This review equips readers with a detailed, structured foundation for applying digital‑twin frameworks in diverse IoT settings, highlighting opportunities for extending research into interoperability and modular twin design.