While routine macroscopic placental assessment is performed in all deliveries, anatomopathological examination is reserved for selected cases due to logistical and resource constraints. This study explores whether deep learning models analyzing macroscopic photographs could provide preliminary morphological characterization to support clinical decisions regarding anatomopathological examination requests. A dataset of 156 annotated images was expanded to 374 through data augmentation, combining standardized and non-standardized acquisition conditions. Three architectures were evaluated: YOLO11 for instance-level analysis, and ResNet34 U-Net and EfficientNet-B0 U-Net for semantic segmentation. Results showed that YOLO11 performed well on large, well-defined structures, while U-Net–based models effectively characterized broader regions. Standardized image acquisition improved the Dice score by 15%. These findings support the feasibility of incorporating AI-based photographic analysis into routine clinical workflows for placental evaluation.