From the perspective of telecommunication, next-generation networks will face several challenges of a growing number of users and services, resulting in high-traffic generation with limited network resources. Traditional predictive models, with their limited capacity to extract the intricate spatial-temporal dependencies and the multifaceted topological structures of traffic data, often cannot fully understand the dynamics of traffic flows. Recognizing the potential of Graph Neural Networks (GNNs) - which have already demonstrated efficacy in road traffic prediction - this paper delves deep into a comprehensive evaluation of sophisticated GNN-based models, namely DCRNN, ASTGCN, GWN, AGCRN, GMAN, and MTGNN. Each of these models, equipped with its unique architectural innovations, adeptly captures the underlying spatio-temporal patterns inherent to network traffic data, promising a paradigm shift in how we anticipate and manage future network traffic challenges. In particular,we focus on benchmarking these models on the network traffic prediction task using the NetMob23 Data challenge dataset.