Metaheuristic Optimization Review MOR 3066-280X 10.54216/MOR https://www.americaspg.com/journals/show/3850 2024 2024 School of Finance and Economics, Jiangsu University, Zhenjiang, People's Republic of China Shahid Shahid The increasing number of cyber security threats, notably ransomware and malware, make traditional methods ineffective, hence the need for intelligent methods. This literature review delves into the latest advancements in cyber security technologies that leverage artificial intelligence (AI), machine learning (ML), and deep learning (DL) to enhance system defenses. Key focus areas include improving ransomware detection, developing more effective intrusion detection systems (IDS), securing Internet of Things (IoT) networks, and strengthening cryptographic methods. The reviewed studies highlight how AI-driven techniques—such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and adversarial training—automate the detection of threats, optimize cyber security measures, and offer real-time responses to evolving risks. Innovative frameworks like Zero Trust Architecture (ZTA) and AI further bolster security by offering automated threat mitigation and anomaly detection. Furthermore, new metaheuristic algorithms are integrated into IDS systems to enhance the detection rate and minimize false positives. The advanced approaches show how AI could solve the constantly emerging challenges in cyber security and focus on a continuous development approach to make cyber security scalable, robust, and transparent when considering complex attacks. 2025 2025 41 49 10.54216/MOR.040105 https://www.americaspg.com/articleinfo/41/show/3850