 Monkeypox is a viral disease caused by the Monkeypox virus MPXV. It is contagious and can cause various symptoms including skin lesions, rashes, fever, respiratory distress, lymph swelling, and neurological distress. Diagnosis of MPX is typically done through PCR, which involves taking a sample from the skin lesion. However, this procedure is risky for medical staff, as they could become infected with MPXV if they come into contact with the sample. Fortunately, advances in technology such as IoT and artificial intelligence, AI, have made the diagnostic process smarter and safer. Wearable and sensor-equipped IoT devices allow for seamless data collection, while AI techniques use the data to make accurate diagnoses. To address the need for a more efficient and secure way to diagnose MPX, this paper proposes a non-invasive, non-contact computer vision-based method for diagnosing MPX. Deep learning techniques are employed to classify skin lesions as either MPXV positive or negative. Two datasets, the Kaggle Monkeypox skin. This article was authored by Mariam Fahad Al-Mifare, Samabia Taysen, Mamuna Humayun, and others. We are article.tv, links in the description below.