A New Era for Digital Twins: Progress and Industry Adoption

This systematic review highlights the core elements and broad applications of Digital Twin (DT) technology, emphasizing its role in transforming various industries. A DT is a real-time digital replica of a physical system, built on a foundation of key components such as sensors, data acquisition systems, communication networks, and computational infrastructure for real-time processing. Integrated machine learning and analytics enable predictive insights, while edge computing ensures fast, secure data handling. High-resolution imaging, 3D visualization, and scanning technologies enhance modeling accuracy, and blockchain-based cybersecurity frameworks safeguard data integrity. Feedback-driven databases support continuous system optimization. DTs are widely applied across sectors: in manufacturing for predictive maintenance and process efficiency; in healthcare for personalized diagnostics and treatments; in urban planning for smart, sustainable infrastructure; and in energy for operational optimization and renewable integration. They also enhance immersive training, improve the reliability of autonomous systems, and strengthen supply chain resilience. Industries such as aerospace, automotive, oil and gas, transportation, and marine engineering increasingly rely on DTs to boost performance, reduce risk, and support innovation. This review synthesizes current developments and identifies key research directions to advance, scale, and secure next-generation DT systems.