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Against the backdrop of the ever-evolving IT industry, this comparative study explores the differences among various project management methods, highlighting key distinctions between Agile and traditional approaches by evaluating the benefits of Agile and the drawbacks of not adopting agile methods. Agile practices have gained recognition for their adaptability and efficiency, in addressing dynamic industry demands. Our multifaceted approach, which examines the pros and cons of Agile methodologies across various industries employs different machine learning algorithms—logistic regression, linear regression, and decision tree regressor. The study quantitatively measures Agile’s impact compared to other methodologies using prediction probabilities, classifications, confusion metrics, R-squared, and Mean Squared Error (MSE) for performance analysis. Results highlight that linear regression outperforms other models with 71% accuracy and 82% precision. These findings offer valuable insights into understanding Agile’s impact on IT industries, encouraging further exploration and refinements to make informed decisions on project management strategies and fostering future research to enhance IT project success rates.