This research proposes a comprehensive approach for the early detection and mitigation of Distributed Denial of Service (DDoS) attacks in Software-Defined Networking (SDN) environments integrated with Internet of Things (IoT) devices. The objectives encompass the development of an entropy-based detection mechanism, enhancement of detection rates for various DDoS attack types, creation of a mitigation algorithm using stochastic techniques, implementation of adaptive control for dynamic network response, and integration of energy optimization to enhance overall network security and performance. Our proposed model introduces an Entropy-based DDoS Detection Algorithm (EDDA) leveraging entropy metrics to analyze traffic patterns and identify anomalies indicative of DDoS attacks, with strategies tailored for detecting UDP, TCP, and ICMP SYN flood DDoS attacks. To augment traditional methods, we incorporate a novel approach as Integrated DDoS Detection, Mitigation, and Energy Optimization Algorithm (IDMEOA). This method enhances the resilience of the detection system against evolving attack strategies, maintaining high accuracy while minimizing false positives. Through the integration of dynamic thresholding, our model aims to provide a robust defense mechanism against DDoS attacks in SDN_IoT environments, offering a comprehensive framework for enhancing network security and resilience without relying on machine learning or deep learning techniques.