In the rapidly evolving competitive market of civil aviation transportation, maintaining market share in the airline industry necessitates a focus on increasing customer satisfaction. This study addresses the challenge of understanding the intricate, nonlinear dynamics, and interaction effects of various factors influencing airline travel satisfaction. Our primary objective is to employ data-driven methodologies, particularly machine-learning (ML) approaches, to comprehensively investigate the significance of factors impacting airline travel satisfaction. Leveraging a dataset comprising 129,880 customer feedback entries, we utilize machine-learning algorithms to model airline travel satisfaction. A comparative analysis with traditional logistic regressions showcases the superior predictive accuracy of ML algorithms, notably Random Forest reaching an impressive 95.7%. The study identifies the top five critical factors with significant impacts on passenger satisfaction: inflight entertainment, seat comfort, online booking, online support, and customer type. Notably, improving the quality of inflight entertainment from “very bad” to “very good” leads to an almost two-fold increase in satisfied travelers. Similarly, enhancing seat comfort from “very bad” to “very good” results in a 1.29 times rise in customer satisfaction. In addition, employing a k -mode method reveals a non-linear pattern in the influence of service qualities on travel satisfaction. Distinct customer clusters (cluster 1 and cluster 2) exhibit contrasting requirements for services. Service quality improvements significantly enhance the satisfaction of travelers in cluster 2, while those in cluster 1 are more sensitive to passenger characteristics. This research underscores the pivotal role of specific factors in shaping airline travel satisfaction and highlights the effectiveness of machine-learning approaches in understanding and predicting these dynamics. The findings provide actionable insights for the airline industry to tailor services based on customer segments, thereby enhancing overall customer satisfaction and maintaining a competitive position in the market.