Network Intrusion Detection Systems(NIDS) are critical for securing modern network infrastructures; still, being deep knowledge- predicated approaches constantly suffer from high conclusion quiescence, limiting their connection in realtime and edge computing surroundings. This paper presents an optimized Convolutional Neural Network (CNN) predicated NIDS frame designed to achievesub-5 ms anomaly discovery while maintaining high discovery delicacy. The proposed architecture employs depthwise separable complications to reduce computational exodus, INT8 quantization for conclusion acceleration, and channel community to overlap packet internee, preprocessing, and type tasks. Evaluation was conducted on three standard datasets CICIDS2017, UNSW- NB15, and NSL-KDD demonstrating delicacy situations above 96 and increment exceeding 15,000 packets per second on an NVIDIA Jetson Xavier NX edge device. The frame achieved up to 60× hastily conclusion than LSTM predicated births and demonstrated strong zero-day discovery performance, achieving 94.8 recall on unseen Botnet attacks. These results validate the proposed system's capability to deliver both high- speed and high delicacy discovery, making it suitable for deployment in quiescence-sensitive, resource- constrained surroundings. future advancements will explore confederated knowledge for distributed model updates, bettered inimical robustness, and adaptive explainability features to ensure secure decision making in dynamic network surrounds.