Snakebite envenomation is a significant medical emergency requiring rapid and accurate identification of the responsible snake species for effective treatment. In this study, we propose a deep learning-based approach for snake species identification using images of snake bite marks. We employed three neural network models: a custom Convolutional Neural Network (CNN), Inception V3, and VGG16. The dataset includes images of bite marks from six snake species: cobra, coral snake, king cobra, krait, sea snake, and viper. Preprocessing steps such as rescaling, denoising, and data augmentation were applied to enhance the quality and diversity of the training data. The custom CNN model achieved a test accuracy of 13.3% with a test loss of 4.136, indicating challenges in learning from the bite mark images. Inception V3 significantly outperformed the custom CNN, achieving a test accuracy of 96.7% and a test loss of 0.191. VGG16 demonstrated the highest performance with a test accuracy of 99.3% and a test loss of 0.078, highlighting the effectiveness of transfer learning and pre-trained models in complex image classification tasks. Our findings underscore the importance of leveraging advanced deep learning architectures for accurate snake species identification from bite marks. Future research could explore ensemble methods, real-time classification systems, and integration with other diagnostic tools to further enhance the applicability and robustness of these models in medical and herpetological applications.