The rapid growth of web applications has increased the need for robust security measures to protect sensitive data and prevent unauthorized access. Traditional methods for detecting vulnerabilities in web applications often rely on manual testing or signature-based approaches, which are time consuming, error-prone, and less effective against new or evolving threats. To address these challenges, this paper proposes an automated web vulnerability detection framework using machine learning models.