With the increased sophistication of cyber-attacks, there is a greater demand for effective network intrusion detection systems (NIDS) to protect against various threats. Traditional NIDS are incapable of detecting modern and sophisticated attacks due to the fact that they rely on pattern-matching models or simple activity analysis. Moreover, Intelligent NIDS based on Machine Learning (ML) models are still in the early stages and often exhibit low accuracy and high false positives, making them ineffective in detecting emerging cyber-attacks. On the other hand, improved detection and prediction frameworks provided by ensemble algorithms have demonstrated impressive outcomes in specific applications. In this research, we investigate the potential of ensemble models in the enhancement of NIDS functionalities in order to provide a reliable and intelligent security defense. We present a NIDS hybrid model that uses ensemble ML techniques to identify and prevent various intrusions more successfully than stand-alone approaches. A combination of several distinct machine learning methods is integrated into a hybrid framework. The UNSW-NB15 dataset is pre-processed, and its features are engineered prior to being used to train and evaluate the proposed model structure. The performance evaluation of the ensemble of various ML classifiers demonstrates that the proposed system outperforms individual model approaches. Using all the employed experimental combination forms, the designed model significantly enhances the detection accuracy attaining more than 99%, while false positives are reduced to less than 1%.