Abstract Flash drought (FD), so‐named for its abrupt and unforeseen onset, poses a significant challenge to forecasting, as current Numerical Weather Prediction (NWP) shows limited skill in the sub‐seasonal to seasonal timescale (S2S, 2‐week to 2‐month range). Here, we present various data‐driven deep learning (DL) frameworks designed to bridge this S2S FD forecasting gap and uncover underlying drought‐inducing mechanisms via interpretability. We developed multiple spatiotemporal DL models (e.g., ConvLSTM, U‐Net) and a Bayesian model averaging (BMA) ensemble to forecast pentad‐scale (5‐day) Standardized Soil‐moisture Index (SSI), serving as the basis for subsequent FD identification. These models leverage diverse drought‐related precursors, including compound drought‐heatwave, evaporative stress, vapor pressure deficit (VPD), and vegetation conditions. Evaluating performance across basins with varied climate regimes, we found that Artificial‐Intelligence‐based methods offer enhanced SSI forecast reliability over NWP, particularly for weather‐scale (1–3 pentads). Notably, the BMA ensemble provided reliable SSI forecasts up to 12 pentads (∼60 days, spanning the entire FD lifecycle), outperforming advanced physics‐based NWP and pixel‐wise benchmark models. Occlusion heatmap reveals that DL models leverage physically plausible precursors for predicting subsequent FD events. Through SHAP analysis, three primary FD‐inducing patterns were identified: water‐dominated (e.g., precipitation), energy‐dominated (e.g., VPD), and multi‐driver composite. Representative regions are arid climate, snow climate, and near‐equatorial areas (e.g., equatorial and warm climate), with widespread interactions among drivers. This study demonstrates explainable DL models potent tools for advancing SSI forecasting and dissecting complex hydro‐climatological drivers relevant to FD assessment, offering novel insights for improved early warning systems.