Facial landmark detection is a fundamental task in animal behavior analysis, animal welfare, and affective computing. However, existing approaches suffer from limited generalization due to sparse annotations, speciesspecific datasets, and morphological diversity across species. In this paper, we propose SuperAnimalFace, a unified pre-training framework designed to enhance wildlife facial landmark detection by leveraging diverse cross-species datasets. Central to our method is the construction of a panoramic facial landmark superset that enables semantic alignment across heterogeneous annotations. Additionally, we construct the WildLifeFLM dataset, the first largescale, multi-species benchmark with rich 25-point facial annotations. Our framework demonstrates strong zeroshot generalization results with several different backbones and high data efficiency in few-shot learning, achieving competitive performance with as little as 10% fine-tuning data. Experiments on a newly collected PetFace-45 validation set further prove that SuperAnimalFace provides a scalable and robust solution for the detection of animal facial landmarks. The proposed datasets are publicly available to the research community.