As 5G networks become increasingly ubiquitous, they bring complex security challenges to the forefront, especially in the face of sophisticated Distributed Denial of Service (DDoS) attacks. This paper introduces the Distributed Adaptive Workflow Network (DAWN), a forward-thinking network architecture that provides robust defense mechanisms at the edge of the core network. DAWN, functioning as an auxiliary SDN with load-balancing preprocessors and dedicated switches, epitomizes an intelligent system. It aims to harmonize the capabilities of Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Machine Learning (ML) to combat the intricacies of DDoS threats. It leverages advanced algorithmic techniques, such as Decision Trees, Random Forests, and Autoencoders, for acute threat detection and adaptive response. This is particularly pertinent as we stand on the cusp of the 6G era, which promises even more connected devices and heightened security demands. DAWN’s integration of whitelisting, hardware, and virtual preprocessors offers a proactive and intelligent defense strategy, showcasing its potential as a seminal solution for DDoS attacks. Through rigorous literature review and theoretical assessment, we advocate for DAWN’s dynamic architecture with analytically driven machine learning as a cornerstone in the evolution of network security from 5G and beyond. GitHub— https://github.com/allali7/DAWN-SDN