The integration of Artificial Intelligence (AI) into cartography represents a transformative opportunity for advancing mapmaking, geovisualization, and geospatial analysis. This chapter explores the applications of AI in cartography, focusing primarily on two major streams of AI methods: deep learning and generative AI. In particular, notable deep learning methods include Deep Convolutional Neural Networks (DCNNs), Graph Convolutional Neural Networks (GCNs), and Generative Adversarial Networks (GANs), while generative AI methods include Stable Diffusion-based models and Large Language Models (LLMs). These approaches have the potential not only to improve the performance of traditional cartographic design decisions but also to enhance human creativity. Through four example case studies, including map object detection, map generalization, map style transfer, and map evaluation, we illustrate how AI methods could be employed in cartographic studies. Beyond technological advancements, this chapter also addresses the ethical and social implications associated with the use of AI in cartography. Issues such as bias, trustworthiness, commodification, geoprivacy, and transparency are discussed to ensure the responsible use of AI for cartography.