In today’s era where more and more organizations are making the shift from manual to automated testing to incorporating testing earlier in the software development lifecycle. Automation testing plays a key role in continuous delivery. The role of testing within the SDLC seems to be undergone with evaluation over time. Moving shift from Test Driven Testing (TDD) to Continuous Testing (CT) paradigm, the test team faces substantial challenges in maintaining a stable test environment, developing test automation, test execution & orchestration, and test report accuracy. The presentation will emphasize how to use Artificial Intelligence (AI) to overcome these challenges. Machine Learning (ML) is the core of AI, which uses pattern identification by machine learning algorithms on tons of complex information to predict the future trend. AI is going to take testing to the next level by the starch limit of testing in many ways like visual validation, API testing, risk analysis of test coverage, automatically create smart self-healing scripts using spidering. With this shift, there is a need for a testing team need to know not only how to automate, but also analyze and understand complex data structures, statics, and machine learning algorithms.