As technology advances, the challenges in characterization of nanoelectronics devices and materials also increase. To overcome the challenges, relying on only cutting-edge hardware doesn’t help the metrologist in this new generation. Computational power of machine learning (ML), hence artificial intelligence (AI) has come forward as a strong support to modern metrology tools and data analysis. Here we review how AI is reshaping the metrology power for the nanoelectronics manufacturing community, especially in failure analysis and materials characterization. In this chapter, diverse contributions of AI in major metrology tools such as experiment automation, sample identification, data denoising, fast and robust data analysis etc. are discussed with their impact on the characterization and advancement of nanoelectronics.