The exponential increase in the adoption of IoT has rendered these networks considerably more exposed to DDoS attacks. DDoS attacks leverage network performance and device vulnerabilities to cause massive disruptions. Traditional security solutions of IoT have proved to be very weak in the detection and mitigation of sophisticated threats in a timely and efficient way; thus, IoT systems are highly exposed to serious risks. These challenges form a backdrop to the proposed contribution of this paper: an effective AI-driven security framework toward detection and mitigation of DDoS attacks in IoT networks using dynamic fuzz testing with graph neural networks—a way through which the model will be powerful enough to identify and nullify activities in real time. It leverages NS3 for realistic diversified network traffic flow, which is used in the training of the GNN model. This makes the framework ready for a wide range of simulated traffic patterns and attack scenarios as a well-prepared GNN against real-world conditions. The model is then deployed into a real environment with the network traffic monitors’ identification of the DDoS attack and its details. The model mitigates the attack without affecting legitimate IoT operations. The proposed framework demonstrated 74% detection accuracy and 95% mitigation success in trials. These results also outline the scalability and adaptability capability of the framework, extending its capability to address problems located in the IoT landscape both in the present and future. Thus, the proposed framework offers full protection integral to ensuring the integrity, availability, and reliability of IoT networks through the combination of AI with dynamic fuzz testing. Therefore, they will be an intrinsic part of IoT architectures in the coming years because they guarantee high-quality security from current and future threats due to DDoS attacks.