In modern digital era, cybersecurity threats are increasing exponentially, posing major obstacles network security. Legacy intrusion detection systems (IDS) often fail to detect sophisticated cyberattacks because they follow rule-based detection mechanisms with limited flexibility rule-based mechanisms. This paper proposes an optimized computational learning models toward network intrusion detection that enhances recognition performance, efficiency, and self-learning capability of existing IDS frameworks. The conceptualized framework leverages supervised learning algorithms combined with feature engineering and dimensionality reduction techniques to improve real-time attack detection. We use widely accessible benchmark datasets such as NSL-KDD as well as CICIDS2017 for training as well as examination. Multiple ML classifiers, encompassing support vector machine (SVM), deep learning (DL), along with random forest (RF) models (LSTM & CNN), are implemented and compared. The model is optimized using hyperparameter tuning and feature selection techniques to enhance precision and recall while minimizing false-positive rates. Experimental results demonstrate that our optimized ML model achieves higher detection accuracy (above 98%), outperforming traditional rule-based IDS and baseline ML models. The results are validated utilizing performance metrics encompassing precision, F1-score, recall, accuracy, and ROC-AUC curves. Additionally, testing of model is done in real-world simulated setting to ensure scalability and effectiveness in detecting zero-day attacks. This research highlights significance of artificial intelligence (AI)-driven security mechanisms in proactively identifying cyber threats and strengthening network security. Upcoming studies may integrate federated learning and real-time anomaly detection methodologies to further augment IDS performance.