(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The exponential growth of mobile data traffic and the densification of wireless infrastructure demand predictive capabilities that extend beyond traditional statistical models. Digital twin technology has emerged as a transformative paradigm enabling the creation of a synchronized virtual representation of physical network elements, their states, and environmental dynamics. When coupled with spatial-temporal deep learning architectures, digital twins offer a powerful framework for near-real-time traffic forecasting that captures both the complex spatial dependencies across base stations and the temporal evolution of usage patterns. This paper provides an in-depth systems-level analysis of digital twin-assisted wireless network traffic prediction, emphasizing architectural trade-offs, infrastructure deployment, governance, robustness, fairness, and long-term sustainability. We examine the integration of graph neural networks, convolutional recurrent architectures, and attention mechanisms within a closed-loop twin environment that continuously ingests measurement data, updates model states, and reconfigures resource allocation. The discussion spans cross-domain comparisons with smart manufacturing and urban digital twins, highlighting the unique challenges of wireless environments such as mobility-induced topology changes, multi-scale temporal burstiness, and stringent latency constraints. Furthermore, we address critical policy implications surrounding data provenance, model drift under adversarial conditions, algorithmic fairness in predictive resource assignment, and the carbon footprint of large-scale twin orchestrations. By connecting technical design choices with broader socio-technical considerations, this work outlines a sustainable and responsible trajectory for embedding intelligence into next-generation network operations.