The reliability of a software product depends on the generation of high-quality test cases. Manual methods for test case generation are prone to errors and often result in test cases of lower quality. Automated test case generation is an approach that ensures the production of high-quality test cases, resulting in maximum coverage and minimized errors. Modern automated test case generation employs Natural Language Processing (NLP), enabling the processing and interprets requirements that can be understandable by humans. This study examines natural language processing (NLP) methods for automated test case generation, including large language models like ChatGPT, OpenAI frameworks and rule-based systems which precisely evaluate and interpret requirements that are readable by humans, highlighting how they enhance the robustness and efficiency of software. Through the use of relevant techniques and tools, this study explores how NLP-driven automation will improve test case quality, coverage, and execution efficiency. This research highlights the integration of natural language processing (NLP) into automated test case generation to enhance software testing by improving the efficiency, accuracy, and overall reliability of software quality assurance processes.