A Comprehensive Review of AI-Powered Digital Twin Technologies

Digital twin (DT) technologies have rapidly evolved as a cornerstone of modern cyber-physical systems, enabling real-time monitoring, simulation, and optimization of physical assets through their virtual counterparts. The integration of Artificial Intelligence (AI) into digital twins has further enhanced their capabilities, transforming them from passive replicas into intelligent, adaptive systems capable of decision-making and predictive analytics. This review provides a comprehensive examination of AI-powered digital twin technologies, covering their foundational concepts, system architectures, enabling AI techniques (e.g., machine learning, deep learning, reinforcement learning), and diverse applications across industries such as manufacturing, healthcare, energy, and smart cities. The paper also highlights the key benefits of AI integration, including improved accuracy, autonomy, and scalability, while addressing current challenges such as data quality, interoperability, model complexity, and real-time responsiveness. Finally, we outline emerging trends and propose future research directions aimed at advancing intelligent digital twin systems for next-generation applications. This review serves as a valuable resource for researchers, practitioners, and stakeholders seeking to understand and leverage AI-driven digital twin innovations.