Worldwide, potato blight—which includes both early and late blight—is a widespread disease that significantly reduces crop yields. Early identification of blight outbreaks is essential for managing the disease effectively and reducing financial losses. In-depth research on a novel use of convolutional neural networks (CNNs) for early detection of late and early blight in potato plants is presented in this paper. The study covers many important facets of the CNN-based system’s development and deployment. In order to ensure effective feature extraction and classification, the paper outlines the process of choosing an ideal CNN architecture suitable for complexities of potato leaf imagery. The paper discusses the process to create training dataset and optimization strategies to improve the performance of CNN model and generalization abilities. The effectiveness of the suggested system is assessed through extensive testing utilizing a variety of performance measures, such as accuracy, precision. By providing a sophisticated and automated method for early blight detection in potato crops, this research substantially advances the field of precision agriculture and may improve crop health and yield through prompt intervention and efficient resource allocation.