Agriculture has played a pivotal role in human civilization throughout history and will continue to do so in the future. Therefore, it is essential to prioritize extensive research in this field to assist farmers in effectively managing their crops and maximizing profitability. Significant research has been dedicated to deep learning, machine learning, and artificial intelligence. This article specifically addresses the topic of papaya fruit diseases. It will utilize various image pre-processing techniques such as resizing, rotating, and random flipping, alongside several Convolutional Neural Network (CNN) models like ResNet, VGG16, AlexNet, GoogleNet, or MobileNet, to extract meaningful features. These models will undergo training and testing using advanced deep learning classification methods such as SVM, Decision Tree, Naive Bayes, KNN, and others. Each CNN model and classification technique will produce distinct results, facilitating researchers in selecting the most effective image pre-processing methods, CNN architectures, and deep learning classification approaches. Among all the models evaluated, the Decision Tree algorithm demonstrated the highest performance, achieving superior accuracy i.e 93% as compared to the other methods.