Wildlife species identification from camera trap images presents significant challenges due to varying lighting conditions, environmental occlusions, and dataset imbalances. Traditional manual identification methods are time-consuming and impractical for large-scale biodiversity monitoring. This study proposes an automated deep learning-based approach using ResNet-101, a residual neural network known for its effective feature extraction capabilities. The model is trained on an 8-class wildlife dataset, including species such as antelope_duiker, bird, civet_genet, hog, leopard, monkey_prosimian, rodent, and blank frames, achieving an accuracy of 82.38%. To enhance performance, data augmentation, fine-tuning, and preprocessing techniques, including resizing and normalization, are applied, improving the model’s generalizability across diverse environmental conditions. A comparative analysis with alternative architectures, Resnet-101 and Convolutional Neural Network (CNN) highlights trade-offs in accuracy, computational efficiency, and real-world applicability. Evaluation metrics such as precision, recall, and F1-score are reported to provide a comprehensive assessment of the model’s effectiveness across species categories. The proposed method offers a scalable and automated solution for wildlife monitoring, reducing manual intervention and accelerating species identification for conservation efforts. Future work will focus on improving classification accuracy for rare species and optimizing the model for real-time deployment in resource-limited environments.