Direction Modulated Brachytherapy (DMBT) enables conformal dose distributions. However, clinicians may face challenges in creating viable treatment plans within a fast-paced clinical setting, especially for a novel technology like DMBT, where cumulative clinical experience is limited. Deep learning-based dose prediction methods have emerged as effective tools for enhancing efficiency. To develop a voxel-wise dose prediction model using an attention-gating mechanism and a 3D UNET for cervical cancer high-dose-rate (HDR) brachytherapy treatment planning with DMBT six-groove tandems with ovoids or ring applicators. A multi-institutional cohort of 122 retrospective clinical HDR brachytherapy plans treated to a prescription dose in the range of 4.8-7.0Gy/fraction was used. A DMBT tandem model was constructed and incorporated onto a research version of BrachyVision Treatment Planning System (BV-TPS) as a 3D solid model applicator and retrospectively re-planned all cases by seasoned experts. Those plans were randomly divided into 64:16:20 as training, validating, and testing cohorts, respectively. Data augmentation was applied to the training and validation sets to increase the size by a factor of 4. An attention-gated 3D UNET architecture model was developed to predict full 3D dose distributions based on high-risk clinical target volume (CTVHR) and organs at risk (OARs) contour information. The model was trained using the mean absolute error loss function, Adam optimization algorithm, a learning rate of 0.001, 250 epochs, and a batch size of eight. In addition, a baseline UNET model was trained similarly for comparison. The model performance was evaluated on the testing dataset by analyzing the outcomes in terms of mean dose values and derived dose-volume-histogram indices from 3D dose distributions and comparing the generated dose distributions against the ground-truth dose distributions using dose statistics and clinically meaningful dosimetric indices. The proposed attention-gated 3D UNET model showed competitive accuracy in predicting 3D dose distributions that closely resemble the ground-truth dose distributions. The average values of the mean absolute errors were 1.82±29.09Gy (vs. 6.41±20.16Gy for a baseline UNET) in CTVHR, 0.89±1.25Gy (vs. 0.94±3.96Gy for a baseline UNET) in the bladder, 0.33±0.67Gy (vs. 0.53±1.66Gy for a baseline UNET) in the rectum, and 0.55±1.57Gy (vs. 0.76±2.89Gy for a baseline UNET) in the sigmoid. The results showed that the mean absolute error (MAE) for the bladder, rectum, and sigmoid were 0.22±1.22Gy (3.62%) (p=0.015), 0.21±1.06Gy (2.20%) (p=0.172), and -0.03±0.54Gy (1.13%) (p=0.774), respectively. The MAE for D90, V100%, and V150% of the CTVHR were 0.46±2.44Gy (8.14%) (p=0.018), 0.57±11.25% (5.23%) (p=0.283), and -0.43±19.36% (4.62%) (p=0.190), respectively. The proposed model needs less than 5 s to predict a full 3D dose distribution of 64×64×64 voxels for any new patient plan, thus making it sufficient for near real-time applications and aiding with decision-making in the clinic. Attention gated 3D-UNET model demonstrated a capability in predicting voxel-wise dose prediction, in comparison to 3D UNET, for DMBT intracavitary brachytherapy planning. The proposed model could be used to obtain dose distributions for near real-time decision-making before DMBT planning and quality assurance. This will guide future automated planning, making the workflow more efficient and clinically viable.