Abstract Customer satisfaction plays a vital role in the airline industry, as it strongly influences customer loyalty, brand image, and long-term business success. This project aims to analyze airline customer satisfaction by combining Structural Equation Modeling (SEM) with machine learning techniques. Customer opinion data, along with service quality indicators and demographic details, is used to identify the key factors that affect overall passenger satisfaction. A hybrid analytical approach is applied to predict and classify satisfaction levels effectively. The results show that the proposed hybrid model significantly improves prediction accuracy. Among the machine learning techniques used, the Random Forest classifier performs the best, achieving an accuracy of 92%, making it suitable for handling complex and high-dimensional airline data. Other models such as Logistic Regression, Support Vector Machine (SVM), and Decision Tree also produce reliable results, offering a comparative view of their classification performance. The integration of SEM and machine learning provides a balanced framework that supports both interpretability and accuracy. SEM helps in analyzing hidden factors such as emotional satisfaction, perceived value, and customer loyalty, while machine learning models capture complex relationships among service attributes. The study identifies important satisfaction drivers including in-flight service quality, staff behavior, ease of booking, and on-time performance. Feature selection and feature engineering further enhance model efficiency and reliability. This study also contributes to improving data-driven decision-making in airline service management. By combining structured survey data with unstructured customer reviews, the proposed model offers deeper insights into passenger behavior, preferences, and concerns. Additionally, the use of sentiment analysis strengthens real-time prediction and service optimization . Overall, the findings demonstrate that the hybrid SEM–machine learning approach is effective in analyzing and forecasting airline customer satisfaction. The results provide useful recommendations for airline management to improve customer retention, operational performance, and personalized service offerings. The proposed framework can also be extended to related sectors such as hospitality and transportation, supporting the development of smart and customer-focused business analytics systems. Keywords Random Forest, SVR, Logistic Regression, Decision Trees, MLP, Airlines, Customer Satisfaction.