In terms of transportation, the aviation industry is currently the most well-known and widely utilized. This makes it challenging for all airline businesses to keep up and improve their customer service. The most important area where the aviation sector can improve is customer satisfaction. This study combines both theoretical and empirical components that will provide a framework to determine the interplay between many factors of airline customer satisfaction to provide room for development for the airline industry. It emphasizes the value of features that contribute to a high-quality experience, such as luggage handling, online booking simplicity, punctuality, cleanliness, in-flight amenities, and more. Overall, by offering a thorough analytical framework that combines classic service quality parameters with modern technological developments, situational factors, and consumer-centric viewpoints, this research adds to the ongoing discourse on airline customer satisfaction. This study has employed Single Classifiers and Bagging and Boosting techniques. In the single classifiers, the algorithms utilized are Logistic Regression, K- Nearest Neighbours, Decision Tree, and Support Vector Machine (SVM). The dataset was trained, and then the new dataset was tested, wherein the Decision Tree has outperformed all the others by yielding 92% accuracy. In Bagging and Boosting, the techniques used are Random Forest Classifier, Gradient Boosting, Ada Boost, XG Boost, and Cat Boost, in which XG Boost and Cat Boost have achieved the highest accuracies of 94% each. Bagging and boosting are used to enhance the model’s performance of by reducing errors and refining the predictions.