The virus accountable for monkeypox, categorized within the Poxviridae family and orthopoxviral genus, presents a substantial challenge for timely and precise detection. To tackle this, we introduce a novel technique that combines the distinctive non-handcrafted features of four deep learning models, VGG-16, InceptionV3, MobileNetV2, and Exception, to enhance the precision of monkeypox classification. By applying a Support Vector Machine (SVM) with a polynomial kernel, we achieved a notable 95.30% classification accuracy for monkeypox images, demonstrating its potential in the dependable identification of monkeypox cases. Our methodology highlights the effectiveness of integrating diverse deep-learning techniques and emphasizes the practical application of SVM in the context of monkeypox classification.