Wild animals are crucial for environmental compartments because they are crucial for the sustainability and shaping of ecosystems. Prior to the employment of camera traps in ecological research, it was extremely challenging to collect evidence of wildlife forms. Camera trap systems, a method of exploring nature employing automatically activated cameras on the appearance of wild creatures and producing a significant amount of video data, have made it easier to track wild animals. Since a few decades ago, the study of identifying wild animals has been active. Deep learning algorithms can aid environmental scientists in detecting different and analyzing images obtained from surveillance cameras, enhancing their study capacity and effectiveness. Among the machine learning architecture’s most statistically effective techniques for classifying images using visual data is the convolutional neural network (CNN). However, the method of analysis is improved and accelerated using picture identification and advanced analytics in combination with machine learning techniques, especially deep learning models. The proposed work examines in detail how deep learning and its components significantly contribute to classification and the techniques that offer approaches to streamline these procedures.