Armed conflict has contributed to an unprecedented number of internally displaced persons (IDPs) - individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when fluxes of IDPs will cross into an area remains a major challenge for aid delivery organizations. We sought to develop an approach to more accurately forecast IDP migration that could empower humanitarian aid groups to more effectively allocate resources during conflicts. We modeled monthly IDP flow between provinces within Syria and within Yemen using heterogeneous data on food prices, fuel prices, wages, location, time, and conflict reports. We show that our machine learning approach outperforms baseline persistence methods of forecasting. Integrating diverse data sources into machine learning models thus appears to improve IDP migration prediction.