Learning and prediction of geometric quality in AM aims to learn and predict geometric shape deviations of both built and untried product shapes based on a small set of training products. To effectively control shape deviations of new and untried product shapes in AM, we classify the modeling approaches as predicting modeling and prescriptive modeling. While traditional predictive modeling usual makes prediction within its experimental domains, e.g., a class or family of products, prescriptive modeling is able to make prediction of quality of new and untried categories of shapes beyond the experimental scope. This chapter will first introduce predictive modeling approach to predict geometric quality for products with simply 2D geometries. The modeling approach is further extended and generalized for prescriptive small-sample modeling and prediction for 2D freeform products.