Cyber Physical Systems (CPS) integrate computational and physical processes, enhancing efficiency and productivity. However, their growing interconnectedness increases vulnerability to cyber threats, compromising security and safety. Artificial Intelligence (AI) emerges as a vital tool to enhance CPS security. This research paper presents a comprehensive analysis of real-world case studies demonstrating AI’s effective application in securing CPS. We conduct an in-depth analysis of real-world case studies across five diverse CPS domains, including smart grids, autonomous vehicles, industrial control systems, medical devices, and building automation systems. Additionally, this research covers various sectors where CPS security is critical, such as industrial automation, healthcare, transportation, and energy. Each of these sectors demonstrates unique applications of artificial intelligence (AI) for enhancing security, offering insights into how AI can help mitigate risks and reinforce the resilience of CPS against cyber threats. Our study examines AI-driven solutions, focusing on machine learning algorithms’ effectiveness in detecting anomalies and predicting potential threats, automating incident response mechanisms, and enhancing security protocols and compliance. Our case studies demonstrate AI’s potential in improving threat detection accuracy, reducing response times, optimizing security resource allocation, and mitigating CPS-specific threats (zero-day attacks, data tampering, unauthorized access). We highlight successful implementations of AI-powered intrusion detection systems, anomaly detection algorithms, and predictive maintenance strategies. We discuss limitations, ethical considerations, and future research directions, including data quality and availability challenges, explainability and transparency in AI decision-making, addressing evolving threats and adversarial attacks, integrating AI with existing security frameworks, and ensuring scalability and reliability. This research provides a comprehensive perspective on AI’s role in enhancing CPS security. Our findings highlight AI’s potential in mitigating cyber threats and ensuring robust security. We emphasize the need for domain-specific AI solutions tailored to CPS characteristics, integration of AI with traditional security measures, and continuous monitoring and adaptation to evolving threats. By demonstrating AI-driven security solutions’ effectiveness in real-world CPS applications, our research informs the development of more resilient and secure CPS infrastructures.