Recognizing leaf diseases is essential for the economic well-being of any nation. Various parts of a plant can be affected by infectious organisms; however, this study primarily focuses on detecting leaf disease in plants as a research subject. We conducted a thorough investigation on this subject from 2020 to 2024 and discovered that numerous researchers employ different kinds of machine learning models to analyze crop diseases. This research introduces a novel approach utilizing CNN to precisely identify and classify diseased and healthy plant leaves. The study leverages a thorough dataset consisting of high-resolution images of diverse plant species, both healthy and affected by different diseases. The CNN model is meticulously prepared to recognize intricate disease patterns and symptoms, enabling precise classification. The proposed method involves preprocessing the images to enhance feature extraction, followed by the utilization of a deep learning framework designed specifically for image recognition tasks. The effectiveness of this CNN-based approach in real-time disease detection offers a valuable tool for farmers and agronomists. By facilitating early diagnosis, the method enables timely intervention and administration, reducing crop losses and improving yield quality. This paper will be helpful for researchers who are working in this area and looking for various efficient Machine Learning-based classifiers for leaf disease detection. This paper explores how to detect plant leaf diseases using CNN algorithm and provides solutions to this agricultural problem through Machine Learning Technology. Furthermore, the study demonstrates CNNs’ effectiveness in plant disease detection, achieving high accuracy with automatic feature extraction, outperforming previous methods. Results show superior performance, with potential to reduce crop losses through early disease detection.