The Vertical Total Electron Content (VTEC) of the ionosphere is a crucial parameter for describing the distribution and dynamic changes within the ionosphere. The study utilizes Dual Hybrid Attentional UNet (DHA-UNet) model to achieve higher forecasting performance for global VTEC predictions under the condition of data acquisition delays. Initially, this study uses the first Hybrid Attentional UNet (HA-UNet) model to predict the intermediate missing data. The missing data are caused by delays in data processing, making the Global Ionosphere Map (GIM) for the current day unavailable. Subsequently, the predicted results from the first HA-UNet model are concatenated with the input data to serve as the input data for the second HA-UNet model, yielding the final prediction results. The performance of DHA-UNet model is then evaluated under varying solar and geomagnetic activity conditions. Evaluation results demonstrate that the DHA-UNet model exhibits higher forecasting accuracy and stability compared to commonly used temporal and spatiotemporal forecasting models. Compared to CODG VTEC, the DHA-UNet model achieves Mean Absolute Error (MAE) values of 2.60 TECU, 3.07 TECU, 3.78 TECU, and 6.45 TECU during quiet, weak, moderate, and strong geomagnetic storm periods, respectively, in years of high solar activity. In years of low solar activity, the model achieves MAE values of 1.00 TECU, 1.15 TECU, and 1.54 TECU during quiet, weak, and moderate geomagnetic storm periods, respectively. Even during strong geomagnetic storms, 55% of the residuals from the DHA-UNet model fall within the −5.0 TECU to 5.0 TECU range, surpassing other commonly used models. Compared to the C1PG forecasting product, the DHA-UNet model shows particularly notable improvements in accuracy during the spring and winter seasons, as well as in mid- to high-latitude regions.