The advancement of virtual networks such as cloud computing, Software-Defined Networking (SDN), and Network Functions Virtualization (NFV) has enabled seamless access to information globally, providing on-the-go services and improving operational efficiency. However, these advancements also present significant threats to the security of information and data on these virtual networks due to their interconnected nature and large attack surface. Among these threats, Distributed Denial-of-Service (DDoS) attacks are one of the most frequent threats to network security that cause severe damage, which hamper network performance. In a DDoS attack, the attacker overwhelms network resources on the victim’s side by sending a massive number of packets or connection requests to the server using bots, thereby disrupting the server’s functionality. The existing works explore only single supervised learning algorithms or a comparison of multiple individual classifiers for DDoS attack detection, which limits their effectiveness in handling diverse attack patterns. To address this limitation, our work proposes an ensemble learning approach that combines multiple supervised machine learning techniques to combat DDoS attacks. Specifically, our ensemble learning framework integrates Random Forest, Support Vector Machine (SVM), and AdaBoost techniques to detect and identify different DDoS attacks effectively.