This paper proposes a new method of DDoS attack detection by designing an ensemble learning approach combining multiple machine learning algorithms-including Convolutional Neural Networks (CNNs), Random Forest (RF), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and XGBoost. These models all have diverse strengths: CNN performs well in recognizing spatial features about images, LSTM is well suited to recognize time-dependent information, while points of the structure in XGBoost are maximized. Our experiments demonstrate that it significantly improves the detection accuracy compared to using just one type of model. However, we also describe several challenges that this integration approach carries with it, such as higher computational costs and problems in the computation of more complex functions from different models’ outputs. We discuss further possibilities in improving efficiency through techniques like model compression and transfer learning and explore specific applications, such as real-time anomaly detection across various sectors.