 Machine learning algorithms are revolutionizing the way wildlife imagery is processed, enabling faster detection, counting, and classification of animals and their behaviors. However, there is a lack of systematic literature reviews on this topic, which limits our understanding of how these algorithms can be applied to wildlife imagery. To address this gap, we conducted a rapid review and bibliometric mapping of existing literature. The results showed that an increasing number of studies are using convolutional neural networks, CNNs, also known as deep learning, to process wildlife imagery. Most studies have focused on large charismatic or iconic mammalian species, while an increasing number of studies have been published in ecology-specific journals. Collaborative efforts between countries are rare, but there has been some progress in this area. Additionally, sharing of code is limited, with only 20% of studies providing links to analysis code. Finally, much of the published research and focus on animals comes from India, China, Australia, or the United States. We recommend increased collaboration and sharing of approaches to maximize the potential of wildlife imagery and accelerate the transformation of understanding of wildlife behavior and conservation. This article was authored by Nakagawa, Shinichi, Lejish, Mamburjana, Francis, Roxanne, and others. We are article.tv, links in the description below.