A proactive and flexible strategy is required for cybersecurity due to the constantly changing nature of cyber threats. Cybersecurity solutions are investing in cutting-edge deep learning and machine learning defense mechanisms to bolster their defenses against these types of assaults. Threat identification and protection have made more sophisticated use of solutions based on deep learning and ML. This research employs the NSL-KDD dataset to compare and contrast ML and DL models for cyber threat identification. The methodology comprises several steps, the first of which is data pretreatment with feature encoding using one-hot encoding and Z-score normalization, ensuring the uniformity of feature scaling. Then split data into training data set and testing data set after deciding on which features are relevant to identify the best main qualities. The KNN, ANN, NB, and DNN models are some of the models that are used for evaluating, and the primary performance metrics include, F1-score, recall, accuracy, and precision. The KNN model numbers the highest accuracy of 98.24%, together with the highest number precision of 97.99%, recall at 97.91%, and F1-score of 98%. Consequently, the results focus on the importance of KNN in threat detection and the potential enhancement of DL solutions. Moreover, expanding these models to include their potential for real-time threat detectors could offer useful information concerning the practicality and high-speed actuality in high-performance computing environments.