Network Intrusion Detection Systems (NIDS) play a crucial role in safeguarding network infrastructures against evolving cyber threats. Traditional machine learning (ML) models often struggle with the multi-class classification of attacks due to limitations in capturing complex feature dependencies and handling imbalanced data distributions. In this study, we propose an ensemble multi-model approach to address these challenges and enhance the accuracy and robustness of intrusion detection systems. Leveraging deep learning (DL) techniques and ensemble learning methods, our approach aims to improve classification accuracy by effectively capturing intricate feature dependencies and mitigating data imbalance issues. We conduct experiments using NSL-KDD and CICIDS-2017 datasets, employing rigorous evaluation procedures such as cross-validation and comparative analysis with existing models.