This review article discusses the application of artificial intelligence (AI) in the diagnosis of Mpox, a zoonotic viral disease with potential for human-to-human transmission. Mpox is characterized by severe flu-like symptoms and distinctive skin lesions. The paper explores the use of AI-based methods, including machine learning (ML) and deep learning (DL), in detecting and classifying Mpox. It highlights the challenges in applying these technologies, such as the need for high-quality data and the complexity of the disease's clinical presentation. The article also discusses the importance of benchmark datasets in evaluating AI models and outlines the future prospects of AI in improving Mpox diagnosis and management. The review emphasizes the need for further research to enhance diagnostic accuracy and efficiency, particularly in outbreak scenarios. Mpox, first identified in monkeys, is primarily found in remote forested regions of Central and West Africa, but can also infect humans through contact with infected animals or bodily fluids. The clinical presentation of Mpox is similar to smallpox, but it is generally less severe. However, it can lead to severe complications, especially in immunocompromised individuals. PCR is a highly sensitive and specific method used for Mpox diagnosis, allowing for early detection and treatment. Imaging techniques are used when Mpox leads to complications such as pneumonia or encephalitis. ML classifiers have emerged as valuable tools in infectious disease epidemiology, offering the potential to predict and manage outbreaks like Mpox more effectively. The process involves collecting and preprocessing comprehensive datasets, including historical cases, geographical information, and socio-demographic data, to train and validate ML models. The article concludes with a discussion of the future prospects of AI in improving Mpox diagnosis and management.This review article discusses the application of artificial intelligence (AI) in the diagnosis of Mpox, a zoonotic viral disease with potential for human-to-human transmission. Mpox is characterized by severe flu-like symptoms and distinctive skin lesions. The paper explores the use of AI-based methods, including machine learning (ML) and deep learning (DL), in detecting and classifying Mpox. It highlights the challenges in applying these technologies, such as the need for high-quality data and the complexity of the disease's clinical presentation. The article also discusses the importance of benchmark datasets in evaluating AI models and outlines the future prospects of AI in improving Mpox diagnosis and management. The review emphasizes the need for further research to enhance diagnostic accuracy and efficiency, particularly in outbreak scenarios. Mpox, first identified in monkeys, is primarily found in remote forested regions of Central and West Africa, but can also infect humans through contact with infected animals or bodily fluids. The clinical presentation of Mpox is similar to smallpox, but it is generally less severe. However, it can lead to severe complications, especially in immunocompromised individuals. PCR is a highly sensitive and specific method used for Mpox diagnosis, allowing for early detection and treatment. Imaging techniques are used when Mpox leads to complications such as pneumonia or encephalitis. ML classifiers have emerged as valuable tools in infectious disease epidemiology, offering the potential to predict and manage outbreaks like Mpox more effectively. The process involves collecting and preprocessing comprehensive datasets, including historical cases, geographical information, and socio-demographic data, to train and validate ML models. The article concludes with a discussion of the future prospects of AI in improving Mpox diagnosis and management.