Flash droughts are characterized by a rapid decline in soil moisture and represent one of the most critical emerging hydroclimatic hazards in semiarid regions. This study integrates meteorological and satellite-derived soil moisture data using a deep learning U-Net model to detect flash drought events in Northeastern Brazil (NEB) during 2015-2023. The U-Net model achieved strong agreement with SMAP Level-4 observations, with spatial correlations exceeding 0.6, RMSD values below 0.04 m(3)/m(3), and Nash-Sutcliffe Efficiency (NSE) values > 0.5 across most of the domain. Flash drought events were identified based on a rapid soil moisture drop from the 40th to the 20th percentile within four pentads (20 days). The model accurately reproduced the observed spatial and temporal variability of flash drought frequency and duration, particularly under the influence of ENSO and Atlantic SST anomalies. The highest event frequency occurred in the semiarid interior (Sert & atilde;o), with 4-6 events per year on average. These results demonstrate the potential of deep learning for high-resolution flash drought monitoring and contribute to improving drought early-warning systems and climate-adaptation strategies in data-scarce regions such as NEB.