Abstract
Digital Twins represent virtual replicas of real-world objects, processes, or systems, enabling continuous real-time monitoring, dynamic adaptation, and predictive analytics. Their integration has become central in industrial, urban, and transportation domains under the paradigms of Industry 4.0, the Industrial Internet of Things (IIoT), and smart cities. However, effectively harnessing the torrents of time-series data and complex system states integral to Digital Twins (DTs) requires advanced analytical tools. Machine learning (ML) methods-ranging from classical ensemble models to deep learning architectures and reinforcement learning-provide powerful mechanisms for anomaly detection, failure prediction, operational optimization, and scenario modeling. This review explores the essential ML algorithms, their application contexts, and the supportive infrastructures required for implementing ML-driven DT solutions. We also discuss interpretability techniques, address security and concept drift challenges, examine the role of MLOps in ensuring reliable deployment, and highlight future research directions, including physics-informed ML, multimodal data integration, and advanced transformers for time-series forecasting.