Haemonchus contortus infection is a major cause of anemia in goats and can reduce productivity, reproductive performance, and animal welfare. The FAMACHA© system supports on-farm anemia screening by visually assessing ocular conjunctival color, but scoring can vary with evaluator experience, and lighting conditions. This study developed a smartphone-oriented deep learning framework for FAMACHA©-based anemia screening in goats using ocular conjunctival images. The dataset included 1995 FAMACHA©-labeled images from 141 meat and dairy goats collected across three acquisition campaigns. For the binary screening task, FAMACHA© scores of 4–5 were assigned to the anemic screening class, whereas scores of 1–3 were assigned to the non-anemic screening class. The pipeline combined U-Net-based conjunctiva segmentation with classification using six ImageNet-pretrained backbones: ResNet-50, ViT-B/16, Swin-T, EfficientNet-B0, MobileNetV4-Small, and MobileViT-v2-1.0. Models were evaluated using animal-level dataset splitting, nested cross-validation, validation-selected thresholds, goat-level cluster bootstrap confidence intervals and goat-block paired permutation tests. U-Net with a ResNet-34 encoder achieved a foreground IoU of 0.7342 and a Dice coefficient of 0.8372 for conjunctiva segmentation. On the held-out test set, ViT-B/16 achieved the highest anemia recall (0.924), accuracy (0.861), and anemia-class F1-score (0.879). However, MobileNetV4-Small performed closely, with 0.847 accuracy, a 0.861 F1-score, and the highest specificity (0.825). U-Net-ResNet34 combined with MobileNetV4-Small yielded the lowest estimated combined inference latency of 479.04 ms per image, calculated from separately measured segmentation and classification latencies on a Samsung SM-A556E smartphone using TensorFlow Lite. A Pearson’s correlation analysis showed a strong positive association between fecal egg count and FAMACHA© score (r = 0.7082, R² = 0.5015, p < 0.0001). These results suggest that ocular-conjunctival imaging can support FAMACHA©-based anemia screening in goats, with ViT-B/16 serving as the strongest recall-oriented model and MobileNetV4-Small as a practical candidate for low-latency on-device inference under the tested smartphone configuration.