This study applies Natural Language Processing (NLP) techniques to enhance sustainable practices within the airline industry by transforming unstructured customer feedback into actionable insights. Using Delta Airways customer reviews as a case study, the research integrates Sentiment Analysis, Topic Modeling via Latent Dirichlet Allocation (LDA), and Named Entity Recognition (NER) to evaluate passenger experiences and identify operational pain points. Sentiment Analysis captures the emotional tone of feedback, revealing customer satisfaction trends, while Topic Modeling uncovers recurring themes such as flight delays, baggage issues, and service quality. NER extracts entities like locations, organizations, and time references to provide contextual depth. Using AI-driven methods helps in identifying airline service failures and reputational risks over time. By analysing travelers’ recurring concerns and feedback, airlines can improve customer service and reduce inefficiencies. Thus, they enhanced sustainable operational strategies. This paper demonstrates how NLP techniques can contribute to data-informed decision-making for sustainable business and customer experience improvements.