<h3>Purpose/Objective(s)</h3> For cervical cancer treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT), ideally, the treatment optimization and evaluation should take the accumulated dose distribution into account. However, in current clinical practice different treatment parts are linked to different planning images and optimized independently, resulting in multiple dose distributions. In this study, we used a combination of deformable image registration (DIR) and deep learning techniques to predict the accumulated dose for assisting treatment planning and dose estimation. <h3>Materials/Methods</h3> The dose distributions of both EBRT and BT plans from 32 cervical cancer patients were used for this study. Firstly, the BT images were registered to the EBRT images using DIR to obtain the accumulated dose distributions. Then, a deep learning model based on a 101-layers ResNet was trained to predict pixel-wise dose distributions. The input data was combined anatomic maps including the CT images, structure maps and distance maps. The output data was the corresponding accumulated dose maps. We used 5-fold cross-validation to test the performance of the prediction model. The evaluation parameters included the voxel-based mean absolute error (MAE) and dice similarity coefficient (DSC) of isodose volumes for the prescription dose (PD) of EBRT (45/50 Gy) and the summed PD of EBRT and BT (76.25/82 Gy<sub>EQD2</sub>). Finally, we randomly selected 10 patients to validate the effect of the dose prediction on treatment planning. For each patient, two EBRT plans were separately designed by a single physicist in the cases of unknowing and knowing the predicted accumulated dose distribution. The resulting two EBRT doses were separately accumulated with BT dose for comparison. <h3>Results</h3> In the comparison between the predicted and original dose distributions, the mean MAE of the whole body, normal tissue, bladder and rectum were 3.73±1.58, 3.27±1.06, 6.55±3.10 and 8.05±4.95 Gy, respectively. The mean DSC of the isodose volumes for 45/50 Gy and 76.25/82 Gy<sub>EQD2</sub> were 0.89±0.02 and 0.69±0.09, respectively. Compared to the planning without knowing the predicted dose, the planning with knowing the predicted dose resulted in a lower D2cc (-1.2±0.80 Gy, p=0.04), V40 (-3.23±2.19%, p=0.002), V50 (-2.19±%1.34, p=0.001) and V60 (-0.79±0.82%, p=0.02) of the rectum. For the bladder, the V40 of were decreased by 1.48±0.58% (p<0.001) but other dosimetric parameters had no significant difference. <h3>Conclusion</h3> The accumulated dose prediction could help to estimate and further decrease the doses to organs at risk. The predicted dose is recommended to be used as a reference during treatment planning in order to pursue a superior accumulated dose distribution for the combined cervical cancer treatment.