This review paper provides a comprehensive overview of recent advancements in animal species recognition through the application of transfer learning, with a particular focus on the VGG16 model. The integration of deep learning techniques, specifically convolutional neural networks (CNNs), has demonstrated substantial improvements in the classification accuracy of diverse animal species. By leveraging pre-trained models, researchers have been able to achieve remarkable results, even in scenarios where labelled data is limited. This paper synthesizes findings from various studies that utilized the VGG16 architecture across different datasets, including mammals, birds, and marine species, showcasing its efficacy in capturing intricate visual features essential for species differentiation. Despite the promising outcomes, significant challenges persist, such as the dependency on well-annotated datasets and the need for robust data augmentation techniques. Additionally, the review highlights gaps in current research, particularly regarding the adaptability of the VGG16 model across underrepresented species and ecological contexts. This synthesis of existing literature serves as a foundational resource for researchers pursuing advancements in automated species recognition methodologies.