Software-Defined Networks (SDNs) represent a transformative paradigm in networking, offering flexibility and programmability but remaining particularly vulnerable to Distributed Denial of Service (DDoS) attacks. Current research predominantly emphasizes the vulnerabilities of the central controller, leaving gaps in the understanding of how DDoS attacks impact the control plane and data plane. This research addresses these gaps by exploring the application of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) algorithms, for DDoS attack detection and prevention across both the control and data planes of SDNs. A critical shortcoming in existing literature lies in its academic focus, relying heavily on simulations and idealized conditions to demonstrate theoretical results, with limited attention to real-world industrial challenges. This research bridges this gap by adopting a practical, industry-focused approach to DDoS mitigation. The study uses real-world network environments and datasets, specifically designed for and applied within an SDN framework, rather than relying on synthetic or simulation-based testing. By integrating AI-driven methodologies into both planes of SDNs, this research demonstrates the feasibility and scalability of advanced detection mechanisms in operational environments. The outcomes reflect three years of rigorous investigation and experimentation, providing a practical blueprint for deploying AI-based solutions in industrial SDN contexts. This work represents a significant contribution to the field, offering not only theoretical insights but also actionable solutions for mitigating DDoS attacks in modern SDN infrastructures.