The article discusses the use of deep transfer learning-based neural networks for recognizing and predicting monkeypox from visual data. The study aims to address the challenges of early diagnosis of monkeypox, which can be difficult due to its symptoms resembling those of chickenpox and measles. The research presents a deep learning model based on transfer learning, utilizing the InceptionV3 model, which was trained on a publicly available monkeypox dataset. The model achieved an accuracy of 98%, demonstrating its potential for assisting medical professionals in identifying monkeypox cases. Monkeypox is a viral infection caused by the Orthopoxvirus genus, closely related to cowpox and smallpox. It is primarily transmitted through contact with infected animals or humans, and while it is less contagious than COVID-19, there has been a significant increase in reported cases globally. The virus has been detected in non-African countries, leading to concerns about its spread. The World Health Organization has determined that the current monkeypox outbreak does not constitute a public health emergency. The article highlights the importance of early detection, contact tracing, and isolation measures to control the spread of the virus. Although there is currently no specific medication for monkeypox, the CDC has approved the use of two oral medications for treatment. Vaccination is considered the most effective method of prevention. The study emphasizes the potential of deep learning models in improving the accuracy and speed of monkeypox diagnosis, which is crucial for effective public health responses.The article discusses the use of deep transfer learning-based neural networks for recognizing and predicting monkeypox from visual data. The study aims to address the challenges of early diagnosis of monkeypox, which can be difficult due to its symptoms resembling those of chickenpox and measles. The research presents a deep learning model based on transfer learning, utilizing the InceptionV3 model, which was trained on a publicly available monkeypox dataset. The model achieved an accuracy of 98%, demonstrating its potential for assisting medical professionals in identifying monkeypox cases. Monkeypox is a viral infection caused by the Orthopoxvirus genus, closely related to cowpox and smallpox. It is primarily transmitted through contact with infected animals or humans, and while it is less contagious than COVID-19, there has been a significant increase in reported cases globally. The virus has been detected in non-African countries, leading to concerns about its spread. The World Health Organization has determined that the current monkeypox outbreak does not constitute a public health emergency. The article highlights the importance of early detection, contact tracing, and isolation measures to control the spread of the virus. Although there is currently no specific medication for monkeypox, the CDC has approved the use of two oral medications for treatment. Vaccination is considered the most effective method of prevention. The study emphasizes the potential of deep learning models in improving the accuracy and speed of monkeypox diagnosis, which is crucial for effective public health responses.