Advanced detection mechanisms require the ability to identify cyber threats accurately without producing many false alarms because of increased cyber threat sophistication. Deep Learning under Artificial Intelligence is a leading cybersecurity tool that detects threats immediately in real-time operations. A deep learning system for threat detection is introduced in this research, and it applies to both convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The spatial features of malicious patterns are captured effectively by CNNs, while LSTM networks within RNNs learn attack pattern sequences through time. Evaluation of the designed models focuses on their accuracy rates and examining loss trends using confusion matrices and receiver operating characteristic (ROC) curve analysis. The experimental results show CNN models produce validation accuracy at 89% while reaching an Area Under the Curve score of 0.95, which exceeds RNN models, establishing 88% accuracy and 0.92 AUC score. RNN models can detect changing cyber threats, yet they produce small increases in false positive detection of benign versus malicious behaviour. Research demonstrates that in rapid threat speed identification tasks, CNNs perform better than RNNs for tasks like phishing detection along with malware analysis, but RNNs show advantages when detecting persistent attack patterns. Future research must investigate the creation of cybersecurity threat detection networks that combine CNNs and RNNs to improve performance further. This research demonstrates how deep learning technologies strengthen security defenses through threat analysis and entails guidance for operating decisions in real-time security protection.