This chapter will provide an overview of the various machine learning approaches and techniques proposed to support cell-aware generation, test, and diagnosis. The chapter will focus on the generation of the cell-aware models and their usage for diagnosis. After some backgrounds on conventional approaches to generate and diagnose cell-aware defects, the chapter will present a learning-based solution to generate cell-aware models. Then, the chapter will present a ML-based cell-aware diagnosis technique. Effectiveness of existing techniques will be shown through industrial case studies and corresponding diagnosis results in terms of accuracy and resolution. The chapter will conclude by a discussion on the future directions in this field.