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The increasing adoption of cloud computing, Internet of Things (IoT), digital communication platforms, and interconnected information systems has significantly expanded the cyber threat landscape. Modern organizations face a growing number of sophisticated cyber-attacks, including malware, ransomware, phishing, distributed denial-of-service (DDoS) attacks, and advanced persistent threats (APTs). Traditional Intrusion Detection Systems (IDS) primarily rely on signature-based and rule-based approaches, which often fail to identify previously unknown or rapidly evolving cyber threats. To overcome these limitations, Artificial Intelligence (AI)-based Intrusion Detection Systems have emerged as an effective solution for enhancing cyber security and network protection. This study explores the application of Artificial Intelligence, Machine Learning (ML), and Deep Learning (DL) techniques in modern intrusion detection systems for real-time cyber threat detection and prevention. The paper reviews widely used machine learning algorithms, including Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and ensemble learning methods, as well as deep learning models such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks. These intelligent techniques enable automated traffic analysis, anomaly detection, pattern recognition, and predictive threat identification within complex network environments.