Human monkeypox is an emerging viral zoonotic disease-causing flu-like symptoms, fever, and characteristic rash, challenging for healthcare professionals to diagnose visually. To address this, we propose an OpenCV-based system for classifying skin diseases into Monkeypox and Others. Leveraging a dataset of skin images, we perform data augmentation and preprocessing, followed by feature extraction using GLCM. Our system achieves an accuracy of 85% by employing LGBM classification. We demonstrate the system’s efficacy in correctly identifying Monkeypox cases, yielding a true positive rate of 80%. While promising, our system has limitations, including dataset constraints and inability to classify other skin diseases. Future work aims to enhance system efficiency and overcome these limitations.