Aircraft maintenance is a cornerstone of flight safety, operational continuity, and cost-effectiveness in the aviation industry. Traditional maintenance approaches, relying on scheduled inspections and corrective actions, face limitations in flexibility and efficiency due to their dependence on human intervention. In recent years, artificial intelligence has revolutionized the sector by shifting maintenance management from reactive methods to predictive and data-driven strategies. This transformation has enabled innovative solutions in digital twins, structural health monitoring, automated visual inspections, foreign object debris detection on runways, decision-support systems, and spare parts logistics optimization.

AI-powered predictive maintenance leverages sensor data and deep learning algorithms to estimate the remaining useful life of critical components, minimizing unplanned downtime and improving operational reliability. Digital twin technology creates virtual replicas of aircraft to enable real-time monitoring and proactive maintenance planning. Moreover, automated visual inspection systems reduce technicians’ workload while enhancing inspection accuracy and quality standards.

However, challenges such as data integrity, explainable artificial intelligence, regulatory compliance, and effective human–machine collaboration remain critical to ensuring the safe and sustainable implementation of these technologies.

This chapter highlights how artificial intelligence contributes to improved safety, efficiency, cost reduction, and sustainability in aircraft maintenance. It also provides a forward-looking perspective on the development of human-centric, ethical, and regulation-compliant maintenance ecosystems, outlining how these emerging technologies will shape the future of aviation maintenance practices.