Can co-seismic ionospheric disturbances (CSID) be used to characterize earthquake fault slip? To date, only two studies addressed this question, focusing on the 2004 Sumatra-Andaman and the 2011 Tohoku-Oki earthquakes. In this work, we present a novel method for automatic ionospheric imaging of seismic fault slip using Total Electron Content (TEC) data. The evolution of CSID can be divided into two phases: initial anomaly generation and its propagation from the source area. A key challenge is to separate these phases, as only the initial anomaly is suitable to retrieve earthquake parameters such as magnitude, fault length, and width. Our method uses the earliest CSID arrivals to detect the anomaly and image the seismic fault slip. We use the locations and amplitudes of the first arrivals as well as the dTEC/dt parameter to constrain the geometry of the rupture fault. To develop and evaluate this method, we collected TEC data from 20 earthquakes (successfully imaged 11) obtained by Japan’s GNSS network, which offers the unique spatio-temporal resolution required for imaging. We assess the necessary conditions for effective application and compare the method’s performance under different earthquake characteristics (magnitude, focal mechanism), different non-seismic factor conditions (geomagnetic field-neutral wave alignment, satellite geometry of sounding, background level of ionisation) and different temporal (30s vs. 1s) and spatial (sparse vs. dense networks) resolutions. The estimations of horizontal dimensions of the fault by the ionospheric data show a good agreement with those obtained by traditional approaches (e.g., seismic and tsunami methods). Therefore, GNSS-TEC data can complement a set of conventional/standard tools (e.g., seismometers, tsunami meters) to study the earthquake source area. Besides, potentially, the resulting evaluations of vertical displacements and their dimensions enable near-real-time (NRT; within 10–12 minutes from the onset) imaging of earthquakes by TEC data for rapid tsunami risk assessment.