Due to human activity, many species of birds are now endangered and even facing extinction. Climate change is affecting the longevity and health of seabirds such as albatrosses and deforestation destroys the habitats of wood-dwelling birds such as the mangrove hummingbirds. Flamingos are also a group of birds that are negatively influenced by human activity, especially by fishing and water pollution in sea-facing areas. Many conservation organizations now have flamingos as their targeted protection animal and strive to provide injured flamingos or orphaned fledglings with medical care and housing. But traditional rescue work done by human searching can be quite time consuming and ineffective, and flamingos who are in need of help might not be identified until it is too late.Machine learning algorithms designed to identify flamingos can be used in conjunction with searching drone technology, thus creating a wandering reconnoitering device to locate flamingo flocks in the area as well as to pinpoint flamingos that may be in danger — indicators for danger may include being alone/separated from the flock, lack of movement, abnormal body positions, or presence of scars and wounds. This project aims to train a machine learning model that can separate and identify flamingos against a variety of other birds, including birds that resemble flamingos in some aspects (such as roseate spoonbills). Since adult and juvenile flamingos look very different from each other, future work should also aim to train models to identify juvenile flamingos.