The generation of realistic network traffic is a critical requirement for testing, simulation, and security evaluation in ZigBee-based IoT systems. In this study, we propose a novel framework that extracts sample ZigBee traffic packets and generates semantically meaningful and protocol-compliant synthetic traffic by using Large Language Models (LLMs) such as GPT-4.1 and GPT-5. Unlike traditional rule-based or statistical generators, our approach is data-driven and incorporates sample-based few-shot learning, prompt engineering, and a human-in-the-loop feedback mechanism. To evaluate the effectiveness of the proposed framework, we conduct two sets of experiments. The first focuses on generating unidirectional traffic to emulate typical device-to-hub communication, while the second extends this setup to bidirectional exchanges, capturing realistic request--response dynamics and interaction patterns. The realism of the generated traffic is assessed using a multi-dimensional evaluation framework that includes statistical similarity measures, such as Jensen--Shannon Divergence, as well as protocol compliance, semantic correctness, temporal consistency, and diversity metrics. In addition, we compare the performance of LLM-based generators with classical deep learning baselines, including recurrent neural networks (RNNs) and generative adversarial networks (GANs). We further analyze the computational cost and the impact of internal reasoning effort on traffic generation by systematically evaluating different GPT-5 reasoning configurations. Experimental results show that both GPT-4.1 and GPT-5 successfully learn the temporal and structural dependencies of ZigBee traffic and significantly outperform RNN and GAN baselines in terms of semantic correctness and long-duration generation. Across all experiments, GPT-4.1 consistently generates traffic that more closely resembles real ZigBee traffic while requiring substantially lower computational resources. These findings highlight that low-latency, non-reasoning LLMs can be particularly well suited for highly structured, protocol-constrained network traffic generation tasks, and demonstrate the potential of LLM-based approaches for realistic IoT traffic generation in research and security evaluation.