Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration

The integration of machine learning (ML) into industrial automation is fundamentally reshaping how manufacturing systems are monitored, inspected, and optimized. This review explores the transformative role of ML across three key domains: Predictive Maintenance (PdM), Quality Control (QC), and Process Optimization (PO). By applying machine learning to real-time sensor data and operational histories, advanced models enable proactive fault prediction, intelligent inspection, and dynamic process control—directly enhancing system reliability, product quality, and efficiency. This review also analyzes how Digital Twin (DT) and Edge AI technologies are being leveraged to address domain-specific challenges in Predictive Maintenance (PdM), Quality Control (QC), and Process Optimization (PO). DTs enable virtual replication and simulation of industrial systems, supporting predictive analytics and optimization in low-risk environments. Meanwhile, Edge AI supports low-latency, on-device inference, allowing ML models to operate in real time, even in bandwidth-constrained or cloud-independent settings. Together, these technologies are expanding the practical impact of ML across industrial automation tasks. The paper also catalogs the datasets used, the tools and sensors employed for data collection, and the industrial software platforms supporting ML deployment in practice. It positions ML, DT, and Edge AI as central to the evolution of intelligent and connected manufacturing systems.