The dynamic evolution of mobile access networks, marked by increasing size and complexity, necessitates innovative approaches to automatic anomaly detection and resolution. Traditional manual methods proves insufficient in handling the complexi- ties of modern networks. Consequently, network optimizers must shift focus towards developing algorithmic solutions that enable automation. This thesis emerges from the AI/ML Optimization Program 2024-2026, a collabo- rative research initiative between Telef ́onica’s Radio Access Network Optimization teams and the Universidad Polit ́ecnica de Madrid (UPM). The primary objective is to develop an automated system for managing and monitoring access networks, leveraging data analysis and Machine Learning (ML) techniques to optimize network performance.