This study aims to investigate how AI-driven test case alignment with software requirements affects software maintenance, with a particular emphasis on how it can lower technical debt and enhance software quality. This study will first collect and analyze data from public datasets and bug-tracking systems to achieve this goal. Based on the collected data, this study will fine-tune pretrained LLMs to detect inconsistencies and generate test cases aligned with changing requirements. After this, experimental environments will be simulated to evaluate the AI-driven alignment tool, assessing its applicability and identifying areas for improvement within workflows. This project aims to provide a solid foundation for promoting theoretical research and industrial practices. AI technologies, such as NLP and ML, offer transformative potential for test case alignment by automating the generation of high-coverage test cases and ensuring accurate representation of updated requirements. NLP can analyze textual data to identify implicit requirements and resolve ambiguities, while ML predicts high-risk areas in code based on historical data. AI also dynamically adapts to evolving requirements by detecting changes in real time and updating test cases accordingly. This minimizes delays, reduces manual intervention, and improves defect detection and testing efficiency. Together, these capabilities make AI-driven alignment a powerful tool for addressing modern software testing challenges. Validating the effectiveness of AI-driven test alignment requires real-world experiments and case studies. These tools promise to automate test case mapping, improve coverage, and reduce technical debt, but empirical evaluations are needed to confirm their practicality. Key performance indicators, such as accuracy, recall, precision, scalability, and defect discovery rate, are critical for assessing effectiveness. However, achieving these goals involves several challenges. Measuring accuracy relies on the availability of ground truth data, which is often limited in real-world scenarios. To address this, the study will consider alternative approaches, such as expert validation, leveraging synthetic datasets, or publicly available benchmarks, to provide a robust framework for performance evaluation. Additionally, deploying AI models involves significant computational costs, particularly for large-scale models, requiring careful consideration of resource demands and strategies to optimize efficiency without compromising effectiveness. To mitigate these, this study will explore the potential of lightweight models and optimization techniques to reduce resource consumption while maintaining performance. Data confidentiality and security is another critical challenge, especially when handling sensitive information. The project will explore methods to address privacy concerns and maintain compliance with industry and legal standards, aiming to balance practicality and security in real-world applications. Despite these challenges, such investigations are vital for demonstrating AI’s value in transforming software testing workflows.