Artificial intelligence for sustainable aviation: a review on operational implementations and future perspectives

Abstract This study presents a systematic review of peer-reviewed academic literature to explore the current landscape of artificial intelligence (AI) applications in sustainable aviation operations. Using a qualitative content analysis approach, four main thematic domains were identified, encompassing emission and fuel efficiency, maintenance reliability, infrastructure sustainability and education- or policy-related applications. In addition to thematic synthesis, the study mapped the annual publication frequency, the AI methods employed and the aviation domains targeted. The results reveal an increasing interest in hybrid and deep learning models, such as long short-term memory (LSTM), convolutional neural networks (CNN) and attention-based architectures, particularly in-flight optimisation and delay prediction tasks. AI-based flight optimisation techniques, such as trajectory prediction and adaptive fuel management, contribute to reducing CO 2 emissions through more efficient flight planning and operations. Moreover, predictive maintenance supported by AI-driven digital twin systems has gained prominence due to its potential to reduce downtime and increase safety. The discussion further addresses regulatory challenges, the importance of explainable AI and integration barriers within complex aviation ecosystems. Findings are derived from a focused corpus of 27 peer-reviewed studies, which, although limited in number, offer representative insights into current sectoral trends. This review makes a significant contribution to both academia and industry by offering a comprehensive framework that categorises AI applications and highlights future research directions. Key implications include the need for regulatory harmonisation, real-time decision-support tools, and interdisciplinary approaches that integrate AI with behavioural sciences and sustainability goals.