This research introduces a robust deep learning framework leveraging Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) for the precise identification and classification of plant species and their specific diseases. We train the model to recognize jackfruit, rice, and rose—plants of significant economic importance in Bangladesh—as well as their common diseases, including Jackfruit Algal Leaf Spot, Jackfruit Black Spot, Rice Bacterial Blight, Rice Brown Spot, Rice Leaf Smut, Rose Rust, and Rose Sawfly (Rose Slug). We utilized a large dataset of over 20,000 direct images from Kaggle and Mendeley Data, which underwent rigorous preprocessing and augmentation to enhance training effectiveness. CNNs, which are pivotal in our framework, excel in image classification due to their ability to perform feature extraction directly from raw images, learning hierarchical feature representations that are crucial for recognizing visual patterns. This capability, combined with the depth and breadth of DNNs for learning complex patterns, allows our model to achieve high accuracy, with CNNs notably outperforming DNNs. Specifically, the CNNs demonstrated an impressive 99% accuracy in disease recognition, highlighting their superiority in handling image data. CNNs were used successfully in this study to show how they can be used to automatically find plant species and diseases. This makes early intervention easier and supports sustainable farming in areas that depend on these important crops.