As our dependence on networked systems continues to escalate, ensuring robust cybersecurity and system reliability has become more crucial than ever. Network Intrusion Detection Systems (NIDS) are vital components in safeguarding telecommunication infrastructures against a growing array of cyber threats. However, traditional NIDS are often plagued by high rates of false positives, which can diminish their overall effectiveness and reliability in real-time monitoring. This paper proposes an innovative approach by integrating advanced machine learning (ML) algorithms to significantly enhance the performance of NIDS. Utilizing a diverse and comprehensive dataset, we meticulously trained and evaluated several state-of-the-art models, including Random Forest, XGBoost, and Convolutional Neural Networks (CNN). The findings reveal a remarkable reduction in false positive rates and a marked improvement in detection accuracy. This progress not only strengthens the reliability of NIDS but also establishes a solid foundation for the deployment of these systems in real-world applications. With these advancements, we move closer to achieving a more secure and resilient network environment.