SQL Injection (SQLi) is a critical vulnerability that allows attackers to manipulate databases through malicious queries. To detect such vulnerabilities in web applications, we integrated SQLMAP, a penetration testing tool, with a Random Forest machine learning model. SQLMAP automates vulnerability detection, and its commands are further automated to enable users to perform tests using simple numerical inputs, improving usability and efficiency. Data collected through SQLMAP is analyzed by the Random Forest classifier, trained on labeled datasets of malicious and benign queries, to predict vulnerabilities with high accuracy. Automation streamlines the process, making penetration testing faster and accessible even to non-technical users. This scalable approach can be expanded to detect other vulnerabilities like Cross-Site Scripting or Remote Code Execution, providing an efficient and user-friendly solution that enhances web application security while contributing to broader cyber security advancements.