(untitled) (Journal of Advanced Artificial Intelligence Research, 2026)
The dynamic and heterogeneous nature of traffic in fifth-generation (5G) networks imposes unprecedented demands on resource allocation, latency management, and quality-of-service (QoS) assurance. Traditional forecasting methods, including time-series models and static machine learning approaches, struggle to adapt to the rapid fluctuations and multi-tenancy environments characteristic of 5G infrastructures. This paper proposes a dynamic traffic forecasting framework that leverages the Proximal Policy Optimization (PPO) algorithm, a state-of-the-art deep reinforcement learning technique, to optimize prediction accuracy while maintaining computational efficiency and system robustness. We examine the architectural trade-offs inherent in embedding reinforcement learning agents within network slicing and edge computing layers, emphasizing system-level implications for governance, fairness, and sustainability. The PPO-enhanced approach offers a stable, sample-efficient learning mechanism that balances exploration and exploitation, addressing the convergence and stability issues observed in prior value-based and policy-gradient methods. Through a conceptual analysis of deployment scenarios, including urban macro-cells, industrial IoT clusters, and autonomous vehicle corridors, we illustrate how the PPO framework can integrate with software-defined networking and network function virtualization to enable real-time adaptive forecasting. We further discuss policy and regulatory considerations, such as data privacy, model interpretability, and cross-domain accountability, that arise when deploying reinforcement learning in critical communication infrastructures. The findings indicate that PPO-based traffic forecasting can significantly reduce prediction error variance and improve slice-level QoS assurance, although careful attention must be paid to training overhead, reward sparsity, and the risk of feedback loops. By situating the technical mechanism within broader socio-technical systems, this paper contributes to the discourse on intelligent network governance and the sustainable evolution of 5G and beyond.