Agriculture remains the backbone of many economies, and plant health is essential for food security and high yields. Traditional methods of plant disease identification are slow, inconsistent, and inaccessible for many farmers. To address these challenges, we propose a deep learning-based Plant Disease Detection System that identifies plant diseases through image recognition. Users can upload images of diseased leaves to receive fast, accurate diagnoses and tailored treatments. Utilizing transfer learning, our system fine-tunes the VGG-16 Convolutional Neural Network (CNN) on the Plant Village dataset. The web-based interface, built using Flask, enables easy interaction and disease management. This paper discusses the development and implementation of the system, highlighting its potential to revolutionize plant disease management and support sustainable agriculture. The approach is validated through rigorous performance metrics, and future enhancements are explored.