This systematic literature review examines the integration of Machine Learning techniques within industrial system architectures using OPC-UA for process automation. Through analyzing primary studies published between 2018 and 2024, the review identifies key trends, methodologies, and implementations across various industrial applications. The review identifies a marked increase in research focused on hybrid architectures that integrate Machine Learning with OPC-UA, particularly in applications such as predictive maintenance and quality control. However, despite reported high accuracy rates—often above 95%—in controlled environments, there is limited evidence on the robustness of these solutions in real-world, large-scale deployments. This highlights the need for further empirical validation and benchmarking in diverse industrial contexts. Implementation patterns range from cloud-based deployments to edge computing solutions, with OPC-UA serving as a communication protocol, information modeling framework, and specifically using the finite state machine specification. The review also highlights current challenges and opportunities, providing valuable insights for researchers and practitioners working on intelligent industrial automation.
Velesaca et al. (2025) studied this question.