This project establishes an artificial intelligence (AI)-driven framework to accurately detect sinkhole precursors based on multiple Earth monitoring data sources. Sinkholes are a pervasive and destructive geohazard, threatening critical infrastructure and public safety. Yet accurately detecting sinkhole precursors is challenging due to the subtle and varying nature of these signals. While recent advances in remote sensing and AI have improved our ability to monitor ground movement, existing methods often rely on general-purpose AI algorithms that overlook the specific complexities of sinkhole development. This project introduces novel AI algorithms specifically designed to meet these unique challenges. In particular, the proposed AI-driven framework is capable of integrating different data types, automatically identifying and grouping environmental conditions and adapting detection criteria accordingly. The findings from this research will improve the early detection of sinkhole precursors, supporting public safety and hazard mitigation. The findings will also help city planners make better decisions about zoning, site stability, and infrastructure resilience. Additionally, the project will develop educational materials and outreach programs by organizing a geoscience AI challenge for students and creating new AI-driven geoscience curricula for undergraduate and graduate courses. This research aims to establish advanced multimodal AI methodologies that fuse large heteroge