Cross-Site Request Forgery (CSRF) is a prevalent and serious security vulnerability in web applications, where attackers exploit the trust that a web application has in a user's browser, leading to unintended actions being performed on behalf of an authenticated user. Despite the availability of traditional security measures, CSRF attacks continue to pose a significant threat due to the challenges in detecting and mitigating them effectively. To address these challenges, this research explores the use of machine learning (ML) techniques to enhance the detection and prevention of CSRF attacks in web applications.