This chapter discusses GeoAI applications for interdisciplinary environmental solutions. Interactions of coupled natural–human systems occur at a certain location on the Earth’s surface (i.e., place and its abstraction space) and have distinct space-time patterns. The extraction of geographic knowledge from data has changed significantly with advances in the application of GeoAI and big spatial data (quality, tools, and models). GeoAI includes tools and technologies that enable us to analyze, visualize and understand data pertaining to real-world phenomena and processes in the geographic context, where location plays a crucial role in connecting diverse datasets. The recent widespread advancement of cloud computing and diffusion of AI, including ML methods for analysis and classification of data characterized by 3Ms (multimodal, multidimensional and multiscale data), i.e., images, text, coded information, and data streams, have increased the potential for successful implementation of an integrated geospatial framework to solve complex problems associated with coupled natural–human systems and toward the implementation of “data-driven” decision support systems capable of automatically and autonomously learning decision rules on the basis of the available ‘big’ data. The use of GeoAI, capable of handling large volumes of diverse data, can facilitate cross-disciplinary studies of coupled natural–human systems to gain new interdisciplinary insights into their complexity. This allows today’s scientists to search for ever-smaller needles (granules of data) in ever-larger haystacks (Spatial Big Data collections). This is fortuitous for finding environmental solutions using a geospatially integrated interdisciplinary lens as it can facilitate bridging the gap between disciplines and knowledge across datasets and scales.