The integration of Artificial Intelligence (AI) and Machine Learning (ML) into Quality Assurance (QA) for Automation Engineering represents a transformative shift, leveraging data-driven decision-making and automation across industries. Despite the promising benefits, challenges such as reliability, fairness, and generalizability of ML models remain significant concerns. This paper addresses these challenges by exploring the complexities inherent in assessing and validating ML programs. It identifies obstacles like bias, model robustness, and adaptability to new data, emphasizing the necessity for rigorous testing frameworks. The paper reviews existing methodologies and solutions to enhance the assessment of ML programs, ensuring they perform as intended and meet ethical standards. The study aims to evaluate current QA methodologies and frameworks across different stages of the model life cycle, identify gaps and challenges, and propose innovative QA strategies. It focuses on data quality, model development, deployment, and ethical considerations. Advanced testing techniques such as Metamorphic Testing, Dual Coding, Mutation Testing, Test Adequacy, and DeepXplore are applied to assess the reliability, accuracy, and robustness of ML models. These techniques provide unique insights into detecting faults, improving test coverage, and ensuring model dependability. The findings highlight the importance of integrating diverse testing techniques into ML model development and validation processes. However, the study acknowledges limitations, including the need for adaptation and practical challenges in real-world deployment. The implications of the study are significant for both academia and industry, offering practical insights into enhancing the reliability and security of ML applications. Future recommendations include the integration of ethical testing frameworks to address issues related to bias, fairness, and transparency in decision-making processes.The integration of Artificial Intelligence (AI) and Machine Learning (ML) into Quality Assurance (QA) for Automation Engineering represents a transformative shift, leveraging data-driven decision-making and automation across industries. Despite the promising benefits, challenges such as reliability, fairness, and generalizability of ML models remain significant concerns. This paper addresses these challenges by exploring the complexities inherent in assessing and validating ML programs. It identifies obstacles like bias, model robustness, and adaptability to new data, emphasizing the necessity for rigorous testing frameworks. The paper reviews existing methodologies and solutions to enhance the assessment of ML programs, ensuring they perform as intended and meet ethical standards. The study aims to evaluate current QA methodologies and frameworks across different stages of the model life cycle, identify gaps and challenges, and propose innovative QA strategies. It focuses on data quality, model development, deployment, and ethical considerations. Advanced testing techniques such as Metamorphic Testing, Dual Coding, Mutation Testing, Test Adequacy, and DeepXplore are applied to assess the reliability, accuracy, and robustness of ML models. These techniques provide unique insights into detecting faults, improving test coverage, and ensuring model dependability. The findings highlight the importance of integrating diverse testing techniques into ML model development and validation processes. However, the study acknowledges limitations, including the need for adaptation and practical challenges in real-world deployment. The implications of the study are significant for both academia and industry, offering practical insights into enhancing the reliability and security of ML applications. Future recommendations include the integration of ethical testing frameworks to address issues related to bias, fairness, and transparency in decision-making processes.