Migration data remains scarce, largely inconsistent across countries, and often outdated, particularly in the context of developing countries.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. 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, while reducing prediction errors considerably. We provide evidence based on survey data that our measures indeed reflect genuine emigration intentions. Our findings contribute to different literature by providing 1) a novel way for the measurement of migration intentions, 2) an approach to generate close to real-time predictions of current migration flows ahead of official statistics, and 3) an improvement in the performance of conventional migration models that involve prediction tasks, such as in the first stage of a linear instrumental variable regression.