Artificial intelligence-driven design and deployment of autonomous eVTOL drones for urban air mobility: a comprehensive review of challenges, technologies, and integration strategies

Abstract Urban Air Mobility (UAM) is emerging as a disruptive solution to traffic congestion, environmental degradation, and limited transport infrastructure in densely populated cities. At the core of UAM systems are autonomous electric Vertical Take-Off and Landing (eVTOL) aircraft, designed to operate within complex urban airspaces. This review examines how Artificial Intelligence (AI) serves as a foundational enabler in the development, operation, and integration of eVTOL platforms. It explores AI’s critical role in flight control, real-time navigation, path optimization, fault detection, and predictive maintenance. Enabling technologies such as digital twins, edge AI, and distributed learning frameworks are also reviewed, with emphasis on their role in enhancing decision-making, scalability, and operational resilience. In addition, this paper identifies pressing challenges that must be addressed before large-scale deployment becomes feasible, including limitations in battery technology, fragmented AI integration, regulatory uncertainties, and ethical concerns surrounding autonomy and human oversight. A phased strategic roadmap is proposed, outlining the transition from supervised trials to fully autonomous, AI-managed aerial mobility networks. The paper also highlights the need for standardized testbeds, real-world validation environments, and ethical co-design practices to ensure trust, compliance, and social acceptance. Ultimately, the review underscores the necessity of multidisciplinary collaboration across AI, aerospace engineering, urban policy, and ethics to realize a sustainable and intelligent UAM future.