Machine learning and deep learning are two research fields that have recently gained popularity. Predictions made using these approaches are highly appealing these days. Pet illness prediction is a relatively new concept. In this field, there is virtually little study. Ear Mites is an illness that many pets suffer from, specially, cats. We have developed a program that can predict this condition based on photographs of a cat's ear. We've divided the images into two categories. The first class is ‘infected’, the other class is ‘not infected’. The study's major purpose is to determine whether or not a cat is afflicted with ear mites. The dataset has been accurately gathered, classified, and processed to prevent conflicts. A standard deep learning classifier, the Convolutional Neural Network (CNN), was utilized to get a greater accuracy rate. This model has an accuracy of 88 percent. One of the most sophisticated image identification technique is convolutional neural networks. The findings reveal that CNN-retrieved high-level features which are more discriminative and effective than typical hand-crafted features like LBPH and Haar-WT (Wavelet Transform). As a result, our CNN model is a top-performing technique for identifying ear mite disease and might be employed in real-world situations.