Having the appropriate amount and quality of signaling data to test telecommunication data is a constant challenge for vendors and operators alike. Hence, signaling message generation is often a crucial step during testing. When generating signaling messages, traditional approaches often fall short in meeting the dynamic and complex requirements of 5G core and IoT environments. The current paper proposes to leverage Generative AI, particularly Large Language Models (LLMs), to address these challenges, offering a novel methodology for generating test data that is both diverse and representative of real-world scenarios. Through a series of experiments involving various protocols such as CoAP, HTTP, and data representation formats such as XML, JSON, and SenML, we evaluated the efficacy of LLMs in generating accurate and high-quality test data. Our findings demonstrate that Generative AI can enhance the test data generation process with attention to further needs in improving accuracy.