The timely detection and management of plant diseases is critical in the agricultural industry. Among these, potato leaf diseases can have a major impact on crop productivity and quality. This research addresses the important requirement for rapid and reliable disease detection in potato plants. Using Convolutional Neural Networks (CNNs), a sophisticated deep learning approach, we gain considerable progress in automating the identification process. We demonstrate the models ability to distinguish diverse kinds of disease with an amazing accuracy rate of 98.8% through rigorous experimentation. The use of data augmentation techniques improves the models flexibility to a variety of environmental situations. This breakthrough has significant promise for shaping agricultural methods, providing a powerful tool for early disease intervention, and ensuring global food security.