Plant leaf disease detection is crucial for ensuring healthy crop production and sustainable agriculture. This paper presents an advanced technique for the identification and cataloguing of diseases in plant leaves by means of image processing and machine learning practices. High-resolution images of contaminated and healthy leaves were collected and preprocessed to enhance feature extraction. A convolutional neural network (CNN) model was developed and accomplished to differentiate between various types of leaf diseases with high accuracy. The proposed system was evaluated on a diverse dataset, demonstrating its effectiveness in accurately detecting and categorizing leaf ailments. The results indicate that the model can serve as a reliable tool for farmers and agricultural professionals, aiding in early disease diagnosis and lessening crop losses. This work highpoints the potential of integrating AI-driven solutions in agriculture to improve plant health management and optimize agricultural productivity.