Understanding the spatiotemporal patterns in the relative abundance of soil extracellular enzyme-encoding genes is critical for predicting microbial responses to environmental change and their potential role in nutrient cycling. Yet, integrating novel metagenomic observations with spatiotemporal environmental gradients to infer regional patterns and future trajectories has remained unclear. To address this gap, we applied a machine learning (ML) approach, integrating soil metagenomic data with environmental variables-soil properties, topography, vegetation, and climate-to predict the relative abundance of enzyme-encoding genes for soil carbon (C), nitrogen (N), and phosphorus (P) across surface soils of the continental United States. We assessed potential responses under future emission scenarios (SSP2-4.5 and SSP5-8.5) by comparing a baseline (1985-2014) to a future period (2071-2100). The ML model explained 57%-63% of baseline variation. Precipitation was identified as the most influential factor for the relative abundance of C- and N-degrading enzyme-encoding genes, while slope length, representing horizontal distance that water can travel downslope, was the primary driver for P-degrading enzyme-encoding genes abundance. Projections revealed spatially heterogeneous shifts across continental US ecoregions: the relative abundance of C- and N-degrading enzyme-encoding genes decreased in wetter ecoregions and increased in drier ecoregions under future climate, while P-degrading enzyme-encoding genes abundance decreased significantly in semiarid and Mediterranean ecoregions. This study demonstrates the utility of metagenomic data for mapping soil genetic potential and predicting its regional response to environmental change, to inform ecosystem management strategies.