<p>Nearly 110 million people are forcibly displaced worldwide. However, estimating the scale and patterns of internally displaced persons in real time, and developing appropriate policy responses, are hindered by traditional data streams because they are infrequently updated, costly and slow. Mobile phone location data can overcome these limitations, but it only represents a population segment. Drawing on an unprecedentedly large, high-frequency anonymised dataset of locations from 25 million mobile devices, we develop a novel methodological framework to leverage mobile phone data and produce population-level estimates of internal displacement. We use this framework to quantify the extent, pace and geographic patterns of internal displacement in Ukraine during the early stages of the Russian invasion in 2022. Our results produce validated population-level estimates, enabling real-time monitoring of internal displacement at detailed spatio-temporal resolutions (e.g., daily, small administrative units). The accurate estimations we provide are crucial in delivering timely and effective humanitarian and disaster management responses, prioritising resources where they are most needed. Given access to similar mobile phone data, our methodology can be applied to estimating population displacement in any geographical context globally in situations of humanitarian crisis, namely climate-induced hazards, conflict and epidemics.</p>