The recent advancements in animal monitoring and pose estimation have significantly enhanced the precision and efficiency of ecological research. A notable trend is the integration of advanced machine learning techniques, particularly Transformers and probabilistic models, to address the complexities of animal re-identification and pose estimation in diverse and challenging environments. These innovations are enabling more accurate population counts, detailed behavioral analysis, and robust conservation strategies. The field is also witnessing a shift towards real-time and efficient processing frameworks, leveraging novel architectures like Mamba-based models, to meet the computational demands of large-scale data processing in wildlife monitoring. Notably, the development of synthetic datasets and the incorporation of segmentation masks in pose estimation models are further pushing the boundaries of what is achievable in this domain. These developments collectively underscore a move towards more integrated, scalable, and robust solutions for animal monitoring and pose estimation.