DDoS attacks, also known as distributed denial of service (DDoS) attacks, have emerged as one of the most serious and fastest-growing threats on the Internet. Denial-of-service (DDoS) attacks are an example of cyber attacks that target a specific system or network in an attempt to render it inaccessible or unusable for a period of time. As a result, improving the detection of diverse types of DDoS cyber threats with better algorithms and higher accuracy while keeping the computational cost under control has become the most significant component of detecting DDoS cyber threats. In order to properly defend the targeted network or system, it is critical to first determine the sort of DDoS assault that has been launched against it. A number of ensemble classification techniques are presented in this paper, which combine the performance of various algorithms. They are then compared to existing Machine Learning Algorithms in terms of their effectiveness in detecting different types of DDoS attacks using accuracy, F1 scores, and ROC curves. The results shows high accuracy and good performance