Intelligent Machine Learning in Optical Networks: A Structured Review of Applications, Challenges, and Future Research

Abstract

Optical networks are evolving toward high-capacity and flexible infrastructures capable of supporting rapidly changing traffic demands and diverse services. The increasing complexity of these networks has created a need for intelligent methods that can assist network planning, monitoring, resource management, and operational control. Although machine learning has shown promise in areas such as transmission-quality estimation, failure prediction, resource optimization, and anomaly detection, the deployment of separate models for individual tasks can result in duplicated development efforts, complex maintenance, and limited adaptability. This paper reviews the use of machine learning as a service-oriented approach for supporting intelligent optical-network operation. A structured review is conducted to classify existing applications, examine the machine-learning lifecycle, and analyze the requirements for integrating learning services with network-management functions. The review covers major application areas, including transmission-quality prediction, predictive maintenance, resource and capacity optimization, device performance modeling, anomaly detection, and traffic prediction. The analysis further identifies key deployment challenges related to data quality, model generalization, model drift, interoperability, processing latency, security, and operational reliability. Emerging approaches involving digital twins, edge-based intelligence, federated learning, and autonomous network control are also examined. The findings indicate that service-oriented machine learning can improve the reusability, adaptability, and operational efficiency of intelligent optical networks, but practical deployment requires reliable data pipelines, continuous model management, interoperable interfaces, and mechanisms for trustworthy automated decisions. The study concludes by identifying research opportunities for developing standardized and adaptive learning services capable of supporting continuous optical-network automation.