Cross-Site Scripting(XSS) is a common web application security flaw that enables attackers implant harmful scripts into pages that other users are seeing. These programs may spoof websites, take over browsing sessions, collect sensitive information, and send visitors to dubious domains. xss attacks are considered as one of the most threatening web server attacks as it is accentuated by the Open Web Application Security Project (OWASP) as one of the top 10 web server attacks and ranked from the top 6 according to the cyber security news in 2024. The paper here presents a study to detect the xss attack using both machine learning and deep learning algorithms The model aims to investigate using machine learning and deep learning using different models using SVM, random forest and neural network. Effective XSS detection helps web developers and security professionals safeguard against potential exploits and ensure the safety of user data and interactions on the internet.