One of the most malicious types of cyberattacks is the Cross-Site Scripting attacks, commonly known as XSS attacks. They often make use of network vulnerabilities in the way user inputs are handled and in browser rendering. Through the means of this research, we try to present an innovative approach towards the detection and mitigation of XSS attacks by combining dynamic DOM (Document Object Model) visualization and adaptive real-time strategies. We make use of React.js libraries for the frontend visualization and Node.js for the backend detection, which, when combined, provides a novel comprehensive solution for real-time DOM monitoring and dynamic response to malicious activities. Our proposed approach makes use of a hybrid detection mechanism, which incorporates both static pattern matching techniques like regex and adaptive machine learning models to detect both the existing known XSS threats and emerging ones. The enhanced proposed system offers security analysis and an instantaneous view of malicious manipulations in the DOM by means of a dynamic DOM visualization tool, which enables almost real-time threat detection and mitigation. This study demonstrates the use and effectiveness of our proposed solution with results, thus showing high detection accuracy and ultra-low latency, offering an efficient and scalable solution for real-time XSS defense mechanisms. The potential to enhance web-based application security by automating the detection and mitigation of XSS attacks, reducing manual dependency, and improving the overall protection rate against evolving and growing threats provides for the significance of this work.