Machine learning (ML) and artificial intelligence (AI) are transforming the way we study the cryosphere. These data-driven tools are rapidly increasing in popularity and offer potential impact throughout the scientific workflow, from the way we design studies, observe processes, collect data, model phenomena, and analyse systems to the way we construct and test hypotheses. While ML and AI methods applied across the cryosphere may be originally intended to answer a particular cryospheric question, the solutions developed to solve these specific problems may offer generalisable approaches and transferable insights to issues in other domains of the cryosphere. As such, this session invites contributions using ML and AI from all branches of cryospheric science, including snow and avalanches; permafrost; glaciology; ice caps, ice sheets, ice shelves and icebergs; sea ice; and freshwater ice. We also welcome contributions focusing on dataset development, theoretical research, and community-building initiatives. This session intends to provide a forum for cross-cutting discussions and knowledge exchange, fostering interdisciplinary collaboration and ultimately promoting the efficient and effective application of ML and AI in the cryosphere.