Artificial intelligence is growing fast in many domains, like software testing. This raises questions about how it could improve or even replace traditional methods. This study looks at both traditional and AI-based software testing, showing their advantages and problems. Traditional software testing uses manual processes and automated scripts. It takes a lot of time and human effort to design tests, run them, and analyze the results. These methods are familiar and dependable, but they are not very flexible and require a lot of time and resources. On the other hand, AI-based methods, like machine learning, can create and execute tests on their own. These techniques can find small problems and patterns that traditional methods might miss. They also save time and reduce costs because they can do a lot of the work automatically. Moreover, AI tools can check the results more quickly and find useful information by working with a lot of data and finding patterns. But using AI for software testing also has some problems. For example, AI needs a diverse data to learn correctly. Also, the results it gives can sometimes be hard to understand because it’s not always clear how it works.