Software applications are becoming increasingly complex, making software difficult to perform. The introduction of Artificial Intelligence (AI) in test software has proven to be a potential way to overcome such difficulties. Software testing methods using AI are based on machine learning, natural language processing, and AI technologies which can automate the testing processes, recover its test coverage, and verify the correctness of the test results. This research outlines software testing based on AI, its advantages and disadvantages, and various tools used in this field. This paper also mentions AI-based software testing, future directions, and some of the ongoing research and development efforts in the challenges. In this article, the innovative effects of AI used in testing of software were examined for automatic error correction and detection. As the pressure on accelerated software releases at the expense of quality increases, research issues are based on quality and create the need for new age testing methods that can meet the agile development cycle. The study uses literature research and case analysis of tools and techniques derived from AI-based defect prediction and repair. The critical snack bar identifies the ability of AI to perform complex tasks, process large data records, learn from previous mistakes, and accelerate defect detection and correction. The effects are improving software quality, reducing development costs, and accelerating time to market.