The recent outbreak of monkeypox spread over seventy-six countries, seventy of them not having any prior history of monkeypox cases, because of its complex transmission patterns and high frequency of human-to-human transmission. As a result, it has appeared as one of the most critical health concerns. Therefore, a quick diagnosis is crucial, and in this case, computer-aided lesion detection may help identify suspected cases quickly. In this work, the first three pre-trained architectures, VGG19, ResNet50, and DenseNet121, for the transfer learning approach. Among them, ResNet50 proved a comparatively ideal outcome and diagnostic validity arrived at 97.68%. Then, the system combines the in-depth features and machine learning classifiers to get more effective results. From the experimental outcomes, the research finds that the detection accuracy of ResNet50 + SVM is 99.55%, which is improved by 2.47% from the baseline ResNet50 model. In addition, our proposed system also achieves 99.82% sensitivity, 99.33% specificity, and 99.69% AUC, respectively. Therefore, this research shows that the introduced method can be a beneficial tool for clinical decision-making.