Accurate intraoperative delineation of the aortic root and localization of key anatomical landmarks during transcatheter aortic valve implantation (TAVI) remain difficult because fluoroscopy provides low soft-tissue contrast and is frequently degraded by motion and overlap from catheters and delivery systems. This study developed and validated a multitask deep learning model for simultaneous aortic root segmentation and landmark localization on fluoroscopic images to support image-guided TAVI. A retrospective dataset of 2,895 fully anonymized fluoroscopic frames from 83 patients who underwent TAVI between 2018 and 2024 was used. Expert annotations included binary masks of the contrast-enhanced aortic root and four anatomical landmarks: two aortic annulus points (AA1, AA2) and two sinotubular junction points near the coronary ostia (STJ1, STJ2). We developed BoundaryAwareMANet (BAMNet), a multitask architecture combining an EfficientNet-V2 encoder, an MA-Net-inspired decoder, a coordinate-aware landmark head, and an auxiliary boundary-guidance pathway. Model performance was evaluated using patient-level five-fold cross-validation. Across five folds, BAMNet achieved Dice 0.916+0.011, IoU 0.850+0.018, and Surface Dice@4 mm 0.845+0.031. Landmark localization reached median and mean errors of 7.64+0.33 px and 10.02+0.17 px, corresponding to fold-weighted millimeter errors of 2.03 mm and 2.66 mm after image-specific pixel-spacing conversion. The model produced both segmentation masks and landmark coordinates in a single forward pass, maintaining real-time inference at approximately 63 FPS. Joint segmentation of the aortic root and localization of anatomical landmarks on intraoperative fluoroscopy is feasible.