Modern web applications require robust, scalable and efficient QA methodologies due to their increasing complexity. Dynamic user interfaces, frequent deployments, and changing functional requirements many times can not be well addressed by manual testing or scripting tests approaches. In this paper, we outline an automated quality assurance (QA) framework that combines Model-Based Testing (MBT), a modeling approach to designing and generating test cases based on system models with AI-assisted test generation strategies using the Cypress testing platform. Our approach uses system models to automatically derive test cases, covering all possible behaviors of the application with minimal human assistance. Additionally, it uses AI for test scenario generation, optimization and prioritization based on historical defect patterns, user interaction data and application change patterns. It can break that combination of adaptive testing, accelerated regression cycles, and accurate defect detection. It also supports CI / CD pipelines, allowing users to seamlessly execute automated test suites after a deployment within live development environments. Experiment results show that our proposed system can reduce the time required for designing tests, increase testing coverage and find faults more efficiently when compared to conventional methods of testing. Moreover AI-based test generation greatly enhances maintainability by automatically updating the test scripts if any change occurs in UI or Logic. The paper demonstrates the feasibility of merging MBT withInSession Cypress automation & AI to build future generations QAS for current web applications, providing high software quality and reliability at speed.