Tsunamis generated by large earthquake-induced displacements of the ocean floor can lead to tragic consequences for coastal communities. Ionospheric measurements of Co-Seismic Disturbances (CIDs) offer a unique solution to characterize an earthquake’s tsunami potential in Near-Real-Time (NRT) since CIDs can be detected within 15min of a seismic event. However, the detection of CIDs rely on human experts which currently prevents the deployment of ionospheric methods in NRT. To address this critical lack of automatic procedure, we train machine-learning models (random forests) over an extensive ionospheric waveform dataset to (1) classify ionospheric waveforms between CIDs and noise, (2) pick arrival times, and (3) associate arrivals across a satellite network in NRT. Our model shows excellent classification and arrival-time picking performances (~95% recall, average error < 10 s). This model is the first automatic CID detector which paves the way for the NRT imaging of surface displacements from the ionosphere.