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Web application development and security have always been looked at with the frame of reference of securing the web application from unauthorized user cyber-attacks. The vulnerabilities currently existing in the web application have been attributed either to using an inappropriate software development model to guide the development process or the use of a software development model that does not prioritize security as a primary concern. In recent years, vulnerability prediction techniques have mainly relied upon the availability of data labelled with vulnerability information for analysis and training. For many real-world web applications, past vulnerability data is often not available, or at least not completely. Hence, to address both situations where labelled past data is fully available or not, we can use Artificial Intelligence and Machine Learning to learn and build vulnerability predictors based on hybrid code attributes. Evolving technologies primarily Artificial Intelligence and Machine Learning have changed the landscape of Web application development and application security. The present generation considers end-user engagement with the web application as one of the key priorities. This research work mainly is conducted to investigate how Artificial Intelligence and Machine Learning can integrate with the traditional web application development model and make web applications more secure and interactive. [ABSTRACT FROM AUTHOR]