Despite recent improvements in data collection and measurement, migration data remains scarce, largely inconsistent across countries, and often outdated. This is especially the case in developing and emerging countries. Rapidly growing internet usage around the world provides geo-referenced online search data that can be exploited to measure migration intentions in origin countries in order to predict subsequent outflows well-ahead of official data publication (now-casting). We contribute to the literature by projecting flows using Google Trend Index data (GTI) on migration-specific search terms. Based on fixed effects panel models of migration as well as machine learning and prediction techniques, we show that our approach yields substantial predictive power for international migration flows. We provide evidence based on survey data that our measures indeed reflect genuine emigration intentions. They can hence not only be used in research but may also inform border control or humanitarian aid management and thus matter for policy-makers in both developing and developed countries.