There are about 100 different kinds of snakes in Tamil Nadu, both poisonous and non-venomous. Sixteen species are identified out of them, the top five of which are venomous and the top seven of which are not. Notwithstanding their unfavorable reputation, snakes are essential to the ecology because they help keep pests under control and the ecological balance intact. With an emphasis on a subset of snake species located in the Indian state of Tamil Nadu, this code implements a model for classifying snake species. The effectiveness of four different deep learning algorithms—SqueezeNet, ResNet, SimpleNet, and MobileNet—in classifying different species of snakes is investigated in this experiment. Using a dataset of pictures of snakes, both venomous and non-venomous, that were discovered in the Indian state of Tamil Nadu. The method that works best for this classification task is found through extensive testing and analysis, which takes into account variables like accuracy, precision, and recall. This research intends to inform decision-making in conservation efforts and snakebite prevention techniques by shedding light on the advantages and disadvantages of each algorithm. The implementation and assessment of these algorithms are demonstrated in the accompanying code, which provides a useful framework for future studies on the classification of snake species.