  The advent of novel 5G services and applications with binding latency
requirements and guaranteed Quality of Service (QoS) hastened the need to
incorporate autonomous and proactive decision-making in network management
procedures. The objective of our study is to provide a thorough analysis of
predictive latency within 5G networks by utilizing real-world network data that
is accessible to mobile network operators (MNOs). In particular, (i) we present
an analytical formulation of the user-plane latency as a Hypoexponential
distribution, which is validated by means of a comparative analysis with
empirical measurements, and (ii) we conduct experimental results of
probabilistic regression, anomaly detection, and predictive forecasting
leveraging on emerging domains in Machine Learning (ML), such as Bayesian
Learning (BL) and Machine Learning on Graphs (GML). We test our predictive
framework using data gathered from scenarios of vehicular mobility, dense-urban
traffic, and social gathering events. Our results provide valuable insights
into the efficacy of predictive algorithms in practical applications.
