Predicting the drivers of incursion and expansion of vector-borne diseases as part of early-warning strategies (EWS) is a major challenge for geographically extensive diseases where spread is mediated by spatial heterogeneity in climate and other environmental drivers. Geospatial data on these environmental drivers are increasingly available affording opportunities for application to a predictive disease ecology paradigm provided the data can be synthesized and harmonized with fine-scale, highly resolved data on vector and host responses to their environment. Here, we apply a multi-scale big data–model integration approach using human-guided machine learning to objectively evaluate the importance of a large suite of spatially distributed environmental variables (>400) to develop EWS for vesicular stomatitis (VS), a common viral vectorborne vesicular disease affecting livestock throughout the Americas. Two temporally and phylogenetically distinct events were used to develop disease occurrence–environment relationships in incursion (2004) and expansion years (2005), and then to test those relationships (2014, 2015) at two scales: (1) local and (2) landscape to regional. Our results show that VS occurrence at a local scale of individual landowners was related to conditions that can be monitored (rainfall, temperatures, streamflow) or modified (vegetation). On-site green vegetation during the month of occurrence and higher rainfall four months prior combined with either cool daytime (expansion) or nighttime (incursion) temperatures one month prior were indicators of VS occurrence. Distance to running water (incursion) and host density based on neighboring ranches (expansion) with infected animals were also important in individual years. At landscape-to-regional scales, conditions that favor specific VSV biological vectors were indicated, either black flies in incursion years or biting midges in expansion years. Changes in viral genetic lineage were less important to patterns in VS occurrence than factors affecting the host–vector–environment interactions. In combination with our onset map based on latitude, elevation, and long-term annual precipitation, this year- and scale-specific information can be used to develop strategies to minimize effects of future VS events. This big data approach coupled with expert knowledge and machine learning can be applied to other emerging diseases for improvement in understanding, prediction, and management of vector-borne diseases.