(untitled) (Eureka Journal of Artificial Intelligence and Data Innovation, 2026)
The rapid deployment of fifth-generation (5G) network system brings further complexity, heterogeneity and new performance requirements at the network side as well, which makes it increasingly difficult for telecommunications operators to offer reliable guarantees over QoS. The traditional Rule based and reactive network management mechanism could not be able to easily support static patterns as well as to process such high volumes of data generated in 5G scenarios. In the light of this development, AI has been considered for intelligent failure prediction as well as real-time network optimization. In this paper a machine learning approach is proposed to predict critical failure of 5G network infrastructure and QoS parameters optimization on the fly. The study adopts a quantitative and experimental methodology leveraging synthetic and real-world (5G) datasets that are publicly accessible. Different types of traditional machine learning and deep learning models, e.g., Random Forest, Support Vector Machines (SVM) and Long Short Term Memory (LSTM) networks are used and compared to predict failures. In addition, a QoS optimization module based on reinforcement learning is introduced to dynamically assign the network bandwidth and optimize the service performance.