The cloud-to-edge continuum paradigm has permeated various application domains, including critical urban-city safety systems. In these contexts, anomalies can compromise public safety, for example, by disrupting the communication between smart city infrastructure and vehicles, which aims to prevent accidents at pedestrian crossings. Given these environments’ heterogeneous and large-scale nature, manual recovery from anomalies is not feasible. Machine Learning techniques have emerged as an alternative, supporting a zero-touch approach that enables self-organising and self-healing solutions for anomaly prediction, detection, and mitigation. This paper proposes an Artificial Intelligence-driven, self-organising approach for anomaly management in the cloud-to-edge continuum, integrating both reactive and proactive mechanisms. We evaluate different Machine Learning models, including Random Forest Classifiers, Neural Networks, and Convolutional Neural Networks, to predict node performance anomalies. The simulation results obtained using the COSCO framework showcase the effectiveness of our method. It achieves an F1 score of 73% for multiclass classification, predicting different levels of anomaly severity, and 87% for binary classification, distinguishing between normal and abnormal states.