Automatic test case generation is transforming the QA process by using AI and ML to make test case creation faster and smarter. It reduces manual effort, improves efficiency, and speeds up testing, while increasing test coverage and reducing human error. The process involves defining goals, gathering essential software information, selecting techniques, and leveraging advanced technologies like AI/ML to improve testing efficiency. Automatic test case generation tools can analyze source code, functional requirements, and UI elements to generate meaningful test cases. Popular techniques include model-based testing, state-machine testing, keyword-driven testing, and search-based testing. AI/ML technologies enhance the efficiency and precision of automatic test case generation by prioritizing test cases, generating test data, and continuously learning from changes in software. Cloud-based platforms like LambdaTest offer an AI-powered unified Test Manager for creating, managing, and reporting test cases and test suites. Automatic test case generation is gaining momentum and will likely make testing more efficient, predictive, and effective.