Evaluating student satisfaction in online degree programs is crucial for improving the quality of education. This study applies a linear regression model to identify key factors influencing student satisfaction. This research investigates student satisfaction in online degree programs through the application of a linear regression model. The study aims to identify the critical factors influencing student satisfaction, using a comprehensive dataset that includes demographic, social, and school-related information, such as student grades. Python is utilized for implementing the linear regression model, taking advantage of its robust data analysis and machine learning capabilities. The model's performance, assessed through MSE (0.035) and R-squared values (0.82), indicating high accuracy in predicting student satisfaction. Significant predictors identified include course quality, instructor support, and technical resources, all showing strong positive correlations with student satisfaction. The regression coefficients offer insights into the impact of these predictors on student satisfaction. The study highlights the importance of focusing on course quality and technical support to enhance student satisfaction in online degree programs. The findings underscore the importance of enhancing course quality and technical support to improve student satisfaction in online degree programs. Educational institutions trying to maximize their online learning environments might benefit from the practical insights provided by this study. This research provides useful information for educational institutions trying to maximize their online learning environments and shows how data-driven tactics can be used to dramatically increase the efficacy and quality of online learning.