Machine learning (ML) and deep learning (DL) are enabling scientific breakthroughs of unparalleled scale for ecosystem monitoring. Increasing volume and variety of data types, from field, airborne and satellite instruments, position scientists from across the geo and environmental sciences to utilize ML and DL models to analyze large data volumes, detect patterns and derive mechanistic insights. This session aims to leverage the significant opportunities emerging with ML and DL for analysis of ecosystem structure and function and ecological processes. Contributors to the session will be challenged to summarize the current state-of-the-art and explore solutions to the roadblocks in applying ML and DL. We encourage accelerated advancements in identifying ecological structure and function using a wide range of data, including remote sensing and in-situ observations. The session invites empirical research examining ecological processes using ML and DL. We also welcome contributions advancing functional interpretation and explainability of ML and DL models.