Lyme is a disease that is caused by Borrelia burgdorferi, a bacterium that is spread by ticks. The prevalence of Lyme disease has made it a major public health problem. Immediate identification of the bacteria-carrying parasites is important in preventing the epidemic. This research suggests an alternative approach which uses computer vision to identify Lyme diseases related to ticks. A dataset containing images of ticks was used to create and train a Convolutional Neural Network (CNN) model. Preprocessing and augmentation were done on the dataset with split data into training and testing sets prior to boosting model generalization. The architecture of the CNN consists of convolutional, batch normalization and pooling layers followed by fully connected layers for classification. The Adam optimizer trains the model with a piecewise learning rate schedule. Test set evaluation shows promising results with high accuracy in categorizing tick pictures. Furthermore, this study calculates precision, recall and F1 score metrics which indicates strong performance from this model. A confusion matrix as well as visualization is also used to prove that model can distinguish between different tick classes. This computer vision approach provides a powerful tool for automatic tick recognition thus aiding in early detection as well as prevention of Lyme disease