In today’s fast-changing software development environment, applications are updated frequently, which often causes automated tests to fail. Traditional test automation frameworks are not flexible—small changes in buttons, menus, or workflows can break many test cases. This results in extra maintenance work, delays, and higher costs. To solve this problem, self-healing test automation frameworks have been developed. These frameworks automatically adjust when the software under test changes. Using artificial intelligence (AI), machine learning (ML), and smart algorithms, they detect when a test fails due to a changed element (such as a modified button name or locator) and then find an alternative way to continue the test. Over time, the framework learns from past changes and becomes more reliable. This reduces manual effort, keeps tests running smoothly, and supports faster release cycles in agile and DevOps environments. This paper discusses how self-healing frameworks work, their architecture, and the tools that support them. It also highlights their benefits—such as lower maintenance, stronger test reliability, and faster delivery—as well as their current challenges like occasional incorrect healing and reliance on AI accuracy. Case studies show that self-healing can cut maintenance effort by 40–60%, proving it to be a powerful step toward smarter and more adaptive test automation.