Traditional glass discovery relies on trial and error approaches, thereby leading to a design-to-deploy period of 20-30 years. To address this challenge, in this talk, we will discuss the application of artificial intelligence (AI) and machine learning (ML) in accelerating glass modeling and discovery. Specifically, three aspects where AI and ML can be used include: (i) data-driven models for glass property predictions, (ii) natural language processing (NLP) for extracting information from the glass literature, and (iii) reinforcement learning for accelerated aging of glasses. To demonstrate these aspects, three problems will be discussed. The first focuses on developing interpretable ML models for predicting 25 properties of glasses made of a few among 84 elements of the periodic table. This work covers nearly the entire periodic table for glass-forming elements. The second focuses on extracting information on glasses and other materials from literature to answer specific queries. We will also discuss how MatSciBERT, the first materials-aware language model, can be used to extract information regarding composition–property relationships from the glass literature. Finally, we will discuss how reinforcement learning can be used to generate and study ultrastable glasses obtained after significant aging.