Web accessibility evaluation tools can significantly reduce the time and effort required to carry out website accessibility evaluations. When used carefully throughout the design, implementation, and maintenance phases of Web development, these tools can assist their users in preventing accessibility difficulties, repairing them, and improving the overall quality of Web sites. However, the analysis of existing tool capabilities and the feedback from disabled people has identified the necessity of having the automatic tool that determines the accessibility of web sites and can predict the foreseen accessibility barriers. There are a number of tools and tests that developers can use to validate their web sites but they do not guarantee that pages are accessible, they can only assist in designing them. In this proposal, we evaluate and compare the existing tools, group them by validation coverage area, and propose the guideline to developers on which tool can be used to check different levels of accessibility. Out of these groups, the best tool is picked and they are integrated as a plug-in into the existing framework . Also we propose a novel approach to web pages accessibility testing based on machine learning techniques using the generated report from this framework as training features. It allows our tool to be easily adapted to a wide range of disability groups, or even to individual preferences. In addition, our tool could be used to predict the severity accessibility issues, which could also be based on the preferences of a particular population or indivdual.