The airline industry faces intensified competition that emphasizes the need for enhanced customer satisfaction to ensure sustained success. Machine learning techniques offer effective tools for analyzing passenger feedback to predict satisfaction levels and optimize service delivery. However, predicting passenger satisfaction accurately is challenging due to various factors such as service quality, seat comfort, and flight experiences. This paper proposes a robust ML-based framework to forecast passenger satisfaction effectively. The framework incorporates significant predictive features like flight distance and service quality metrics and employs algorithms including Bagging Classifier, Bayesian Ridge, and ElasticNet. The Bagging Classifier achieves the highest performance with an accuracy of 96% that significantly surpassing existing methods. This research advances predictive analytics in the airline sector, providing valuable insights that assist airlines in enhancing service quality, optimizing operational efficiency, and improving overall customer satisfaction.