The rapid evolution and widespread deployment of Artificial Intelligence (AI) necessitate robust standardization to ensure AI systems’ reliability, fairness, and safety. This chapter explores the critical role of data labeling and testing in the global standardization of AI. Accurate data labeling forms the backbone of AI development, providing the foundation for high-quality, unbiased, and scalable AI models. Rigorous testing frameworks are essential to validate AI performance, security, and ethical compliance. This chapter discusses the challenges and advancements in these areas, highlighting their impact on global AI deployment. Through case studies and an exploration of future directions, we emphasize the importance of continuous improvement and international collaboration in AI standardization, aiming to foster trust, interoperability, and innovation in AI technologies. The specific goal of this chapter is to explore how data labeling and testing contribute to the global standardization of Artificial Intelligence (AI) systems. The chapter addresses key research questions, such as: How do data labeling and testing practices affect AI reliability, fairness, and safety? What challenges exist in standardizing these practices across various industries? Furthermore, it aims to highlight the role of standardization in fostering international collaboration and ensuring the ethical deployment of AI technologies worldwide. This chapter reveals that robust data labeling and comprehensive testing frameworks are critical for ensuring the ethical, reliable, and scalable deployment of AI systems globally. A key contribution of this work is its emphasis on the need for standardized practices to mitigate biases and improve interoperability, fostering greater international collaboration and trust in AI technologies.