Using YOLOv5 for object detection and annotation, the research focuses on counting individual animals in camera trap sequences. This is summarized in the abstract. To increase the precision of animal counting models, the research integrates data from several sources, including Landsat-8 images, I Naturalist, and camera trap data. Data augmentation and fine-tuning procedures are used to solve challenges in wildlife monitoring, such as lighting fluctuations and sparse temporal samples. YOLOv5 is used to provide annotations that are crucial for training animal counting models and for precise item detection and compared with YOLOv8 model to predict which model got the best result and observed YOLOv8 got more accuracy. The project advances wildlife monitoring by improving models, incorporating new data, and exploring cutting-edge methods for accuracy and scalability. Insights that greatly support conservation and animal monitoring initiatives. Technological developments, data fusion, assessment criteria, deep learning applications, and historical viewpoints from prior contests are important points. In increasing the use of cutting-edge deep learning techniques in wildlife monitoring and conservation initiatives. For object detection, and image classification, participants can experiment with cutting-edge technologies. This emphasis on utilizing cutting-edge methods improves the precision and effectiveness of biodiversity assessment procedures. The project's findings, which include tracking wildlife populations, analyzing behavior, and keeping an eye on habitat changes, can support ecological research. Main objective is making wise decisions on conservation efforts requires knowledge of this information. The Realtime example use cases where we can use iwild cam is used are biodiversity monitoring, habitat monitoring.