The increasing complexity of modern software systems and the demand for rapid, high-quality releases have highlighted the limitations of traditional software testing approaches. Manual and rule-based automated testing methods often struggle to provide sufficient test coverage, adaptability, and efficiency in dynamic development environments. This study presents an intelligent framework for automated software testing using artificial intelligence (AI) techniques, supported by a comparative analysis. The report evaluates the efficiency of different testing approaches manual testing, rule-based automation, and AI-based testing-based on metrics such as testing time, test coverage, and defect detection rate. The data examines key AI capabilities, including test case generation, defect prediction, self-healing automation, and test optimization. The results demonstrate that AI-based testing significantly outperforms traditional approaches by reducing testing time, increasing coverage, and improving defect detection accuracy. The framework leverages machine learning and natural language processing techniques to automate test case generation and prioritize testing efforts based on risk and impact. Additionally, self-healing mechanisms enable test scripts to adapt to changes in application interfaces, reducing maintenance overhead. Further analysis reveals that AI-driven test optimization ensures efficient resource utilization by focusing on high-priority test cases, while defect prediction models enable proactive identification of potential issues. These capabilities collectively enhance the efficiency and reliability of the software testing process.