The fast development and implementation of Industrial Internet of Things (IIoT) technologies and modern control systems have reinvented industrial practices, making it possible to monitor industrial processes in real-time, automate them, and become more efficient in the energy, manufacturing, and transportation industries. Nevertheless, the cyber-attack surface has increased, as well, due to this digital transformation, making critical infrastructures vulnerable to advanced attacks such as ransomware or state-sponsored attacks. Artificial Intelligence (AI) has become a capable facilitator of industrial cybersecurity by delivering superior threat detection and predictive analytics and dynamic-oriented defensive procedures according to evolving industrial conditions. The implementation of AI-based solutions, such as machine learning, deep learning, and anomaly detection models, is becoming popular in protecting Supervisory Control and Data Acquisition (SCADA), programmable logic controllers (PLCs) and IIoT devices against the emerging cyber threats. This paper discusses the application of AI in improving resilience in industrial ecosystems due to its ability to detect threats early, automate incident response and reduce downtime. Besides, it looks into major issues like the quality of data, interpretability of models and the performance of AI in integrating with existing infrastructures. The results highlight how AI can transform the field of industrial cybersecurity, and furthermore, that more research will be needed to achieve safe and reliable implementation in life-and-limb industries.