The decline in reptile and amphibian populations due to habitat alteration, invasive species, disease outbreaks, pollution, and climate change highlights the necessity of monitoring these species for effective conservation efforts. Studying and tracking these species is achieved with minimal human interference by leveraging camera traps and deep-learning Convolutional Neural Networks (CNNs). The approach incorporates multiple architectures—DenseNet121, InceptionV3, and Xception—alongside a hybrid model to classify three animal groups from the camera trap images (Ecological Camera Trap Image: Toad, Lizard, Snake) dataset. To prevent overfitting, dropout layers are used within convolutional, pooling, and fully connected layers, key components of CNNs. Various activation functions such as ReLU, Tanh, Sigmoid, and SoftMax, and optimizers like SGD, Adam, AdaGrad, and RMSprop are experimented with to enhance model training. This methodology efficiently extracts and processes image data, achieving notable classification performance. The InceptionV3 model achieves 84% accuracy, the DenseNet121 model reaches 89%, and Xception outperforms others with 90% accuracy. Hybrid models combining DenseNet121-Inception, DenseNet121-Xception, and Inception-Xception yield accuracies of 86%, 68%, and 74%, respectively. In conclusion, while all models demonstrate strong potential for amphibian classification, Xception achieves the highest accuracy of 90% across all classes.