With the growing speed of network traffic and complexity of cyber threats, there is an ever-increasing need for highly advanced network intrusion detection systems (NIDS) that can detect every attack in real time. This chapter suggests a model of ensemble-based NIDS that combines the classifiers SVM, RF, and KNN with voting classifier to enhance detection accuracy and generalization. In order to overcome class imbalance issues commonly associated with network intrusion datasets, the SMOTE technique was applied to the attack and normal traffic samples to balance both datasets. Results: Tested on the widely known benchmark of NIDS research, the “KDD Cup 1999 dataset,” our model showed a phenomenal detection accuracy of 99.96% along with the ideal ROC-AUC score of 1.000, which demonstrated its efficacy in intrusion detection along with low false positives. Additionally, feature importance analysis of a random forest-based method highlights important features to increase the detection. This work establishes that ensemble learning in cybersecurity applications achieves strong performance as the aggregated classifiers are better than the single ones. The proposed model presents an effective scalable high-precision detection mechanism for network intrusion. This is the starting point of integrating deep learning and more advanced techniques in future designs of “network intrusion detection systems (NIDS).”