Advancements in deep learning (DL) and machine learning (ML) have significantly improved the ability to recognize and classify fruit diseases, offering valuable tools for modern agriculture. This research presents a comprehensive framework for recognizing and classifying diseases in economically important fruits—citrus, guava, and mango—by comparing both DL and ML models. We curated a diverse dataset of 13,700 images covering multiple fruit disease classes, including common diseases such as citrus black spot, citrus canker, guava phytophthora, guava scab, guava styler and root, mango anthracnose, mango healthy, and mango stem end rot. The models evaluated include Convolutional Neural Networks (CNNs) and Shallow Neural Networks (SNNs) as DL models, as well as K-Nearest Neighbors (KNNs) and Support Vector Machines (SVMs) as ML models, with a focus on assessing their effectiveness in fruit disease recognition. Our findings highlight the superiority of DL models, particularly CNN and SNN, in handling complex image classification tasks due to their ability to learn hierarchical feature representations. Meanwhile, ML models like KNN and SVM offer practical solutions for simpler classification scenarios, particularly in resource-constrained environments. This research supports sustainable farming by providing accessible and efficient tools for early disease recognition, benefiting both commercial farmers and small-scale agriculturalists in maintaining crop health and preventing economic losses. Additionally, our comparative analysis offers valuable insights into selecting the most appropriate model based on specific agricultural needs, advancing efforts in precision agriculture and food security.