<p>Automated voice disorder classification is useful in clinical assessments. It helps identify voice disorders quickly and supports faster and better management. In this article, the author describes a strong mechanism of voice disorder classification, which is an amalgamation of the deep learning and traditional machine learning approaches. This study utilizes five pre-trained Convolutional Neural Networks (CNNs) to capture high-level characteristics from endoscopic images. Five different classifiers, namely, Support Vector Machine (SVM), Random Forest, k-Nearest Neighbors (KNN), Decision Tree, and Gradient Boosting (XGB) are then used to improve the CNN-based feature extraction model to predict 14 types of voice pathology. This study uses a series of preprocessing techniques on the images so that to enhance the classification accuracy. Contrast enhancement technique is then used to enhance the contrast of the images to allow CNNs to detect important features more effectively. In addition, principal component analysis reduces the number of dimensions by keeping significant features and reducing the complexity of the computations. The study supports these results with comprehensive cross-validation procedure, which is guaranteed to provide the model to work well in generalizing to different data subsets. This two-step system based on CNNs to extract features and the traditional classifiers to perform the final classification has an impressive accuracy in the test of 95.77% with KNN and ResNet50 combined. The reported performance is based on a fixed 80/20 stratified train–test split, where the test set is used for primary evaluation. The ability to use a number of CNN architectures and machine learning classifiers brings greater adaptability and resilience. This demonstrates the potential of the approach to decision-support for clinical analysis in the diagnosis of voice disorders by automation and the significance of the precision and reliability of classification to the effective choice of treatment.</p>