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This systematic review provides a comprehensive evaluation of the core components and diverse applications of digital twin (DT) technology, emphasising its transformative influence across key sectors. A DT is a dynamic digital replica of a physical asset or system, developed through the integration of real-time sensor data, advanced communication protocols, and computational intelligence. Essential elements include machine learning for predictive analytics, edge computing for low-latency decision-making, and high-resolution imaging and 3D visualisation for enhanced model fidelity. Blockchain technologies strengthen data security and integrity, while adaptive feedback mechanisms enable continuous learning and system optimisation. DT applications span various industries. In manufacturing, they enhance productivity through predictive maintenance and process refinement. In healthcare, DTs support personalised diagnostics, treatment optimisation, and telemedicine. Urban planning benefits from DTs in the creation of smart, sustainable infrastructure. In the energy sector, they facilitate grid stability and renewable integration. Additionally, DTs are used in immersive training simulations, autonomous vehicle validation, and resilient supply chain management. Industries such as aerospace, automotive, marine, oil and gas, and transportation increasingly adopt DTs to boost operational efficiency, minimise risks, and foster innovation. This review synthesises recent technological advances and identifies critical research directions for advancing secure, scalable, and interoperable DT ecosystems.
Hasan et al. (2025) studied this question.