Integrating Digital Twins and Machine Learning for Enhanced Predictive Maintenance, Real-Time Analytics, and Cost Savings: A Review

Digital Twins (DTs) have emerged as a powerful approach for creating virtual representations of physical systems and assets, enabling real-time monitoring, control, and optimization. When combined with Machine Learning (ML), these virtual replicas can unlock new possibilities in predictive maintenance, real-time analytics, and cost-efficient operations across multiple industries. This paper offers an in-depth review of the latest developments in DT-ML integration, with a particular focus on manufacturing, healthcare, and smart city applications. We highlight challenges related to scalability, privacy, and data quality, and we examine how the Internet of Things (IoT) can supply the continuous, high-volume data needed to support ML-driven insights. The discussion also covers typical performance metrics—such as accuracy, latency, and resource savings—and explores ongoing research directions that use edge computing, quantum ML, and multi-agent methods to build more adaptable and sustainable systems.