The widespread adoption of telecommunications devices has significantly increased the complexity of network infrastructures, pushing providers to install a large number of devices to ensure communication over long distances. While having fully resilient networks would be ideal, the reality is that the network infrastructure requires maintenance and intervention from the team in charge. The high volume of alarms generated by the infrastructure of service providers poses a significant challenge, requiring the development of automated methodologies to support maintenance operations. This thesis explores the application of Machine Learning and Deep Learning techniques for the supervision and management of telecommunications networks. We focused our analyses on a specific and impactful failure type that potentially affects multiple nodes and cause severe downtime to end users.