Vitiligo, a chronic skin disorder characterized by depigmentation, presents a significant challenge in accurate diagnosis and monitoring due to its diverse manifestations and subjective evaluation. To improve the identification and categorization of Vitiligo lesions from dermoscopic images, this study suggests a simple and robust method that combines deep learning models, such as VGG16 and InceptionV3, with traditional classifiers, such as Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression. The methodology involves pre-processing techniques for image enhancement and feature extraction to optimize model performance. This analysis not only validates the efficacy of deep learning architectures in handling complex image data but also provides insights into the potential advantages of classical classifiers in scenarios with limited training data or computational resources. Moreover, the study explores the synergistic potential of ensemble learning techniques to further enhance classification accuracy beyond individual models. The study also looks into how feature extraction from deep learning models that have already been trained can improve the discriminatory ability of traditional classifiers. The classification accuracies of 93.63% with our VGG16 ensemble, and 97.07% with our InceptionV3 ensemble respectively, demonstrate the better performance of the proposed technique as compared to other state-of-the-art techniques. To the best of our knowledge, this is the first such work in this direction.