This study presents a classification approach for guava fruit diseases using both deep learning and machine learning models. InceptionV3 was employed to extract image features, which were subsequently classified using models such as artificial neural networks support vector machines, k nearest neighbors, random forest, and decision tree. The performance of the models was evaluated in terms of accuracy, F1 score, precision, and recall. Experimental results demonstrate that SVM and ANN achieved the highest performance, with SVM reaching 0.9974 across all metrics and ANN achieving 0.9958. The kNN model also performed well with an accuracy of 0.9924, while random forest and decision tree obtained lower accuracies of 0.9612 and 0.9209, respectively. Confusion matrix analysis further confirmed the superiority of SVM and ANN, with minimal misclassifications across anthracnose, fruit fly, and healthy guava categories. These findings highlight the effectiveness of deep learning-based feature extraction combined with SVM and ANN classifiers for reliable and accurate detection of guava fruit diseases.