With 5G technology, a significant breakthrough in wireless communication, a new age of unmatched speed, low latency, and wide-ranging connectivity is being ushered in. The ability to model and evaluate different 5G traffic scenarios is crucial for network optimization, security, and performance evaluation in this environment. This study introduces Multi_Source traffic, a unique approach that uses Generative Adversarial Networks (GAN) to simulate various scenarios related to 5G traffic. It uses GANs to generate simulated 5G traffic data that reflects a variety of scenarios. Traffic from multiple sources GAN is a modular technology that adjusts to different traffic patterns, user behaviors, and network circumstances. Its main goal is to enable in-depth case studies that mimic actual 5G network characteristics. This study explores Multi_Source traffic’s architecture and training methods and shows how it can generate 5G traffic data simulations that closely mimic real-world network characteristics. Within the evolving 5G ecosystem, the data collection enables data-driven analysis, security testing, and network design and optimization. The employment of Multi_Source traffic GAN is advocated in this article as a critical tool for the future development of 5G technology and all of its potential uses, which will appeal to researchers, network engineers, and business professionals.