The agriculture field contributes significantly to the expansion of economies and population and is essential to the production of high-quality food. Plant diseases can lead to a decrease in species diversity and a significant loss in food output. In many areas, pearl millet is an essential crop that is used as a staple diet and is a major source of nutrients. Diseases that damage its leaves, however, can drastically lower crop output and have an effect on food security. Conventional illness detection techniques, which depend on manual inspection, are labor-intensive, time-consuming, and frequently need specialized knowledge. The work present a deep learning enabled method for automatically identifying and categorizing illnesses in pearl millet plants based on photos of their leaves. In order to take advantage of pre-trained models for feature extraction, we have used cutting edge convolutional neural networks (CNNs) like VGG16, MobileNet, and InceptionV3, coupled with transfer learning strategies. A dataset of photos of pearl millet leaves, including both healthy and diseased samples, was used to train the algorithm. Techniques for data augmentation are used to improve the model's generalization and resilience. Our suggested system was highly accurate in identifying and categorizing common illnesses as blast, rust, downy mildew, and ergot. The results demonstrate the effectiveness of deep learning enabled model for early detection of pearl millet plant diseases, providing a fast, accurate and scalable solution that can assist farmers and agricultural professionals in making informed decisions, thereby mitigating crop loss and improving productivity. This system has the capability to be deployed as a mobile application, providing early disease detection directly in the field.