Mpox, formerly referred to as monkeypox, is a viral zoonotic disease characterised by diverse symptoms impacting the respiratory and integumentary systems. It is transmitted via direct or sexual contact, with immunocompromised individuals and pregnant women being especially susceptible. Following the recent breakout in May 2022, the virus has swiftly proliferated to 120 countries, intensifying efforts to mitigate its rapid dissemination. Consequently, to facilitate the prompt identification of mpox for precise diagnosis and treatment, current research endeavours seek to employ artificial intelligence (AI) and machine learning (ML) approaches to enhance epidemiological surveillance and disease management. This review seeks to examine the significant research on various AI and ML models employed or under development to track and regulate the elevated prevalence of mpox. It will evaluate their efficacy, limitations, and future ramifications for research concerning their successful incorporation into mpox studies. This review's results highlight the potential of AI and ML models to enhance comprehensive mpox management, with findings that establish correlations with optimal clinical options for management. Furthermore, this review emphasises the necessity for monitoring a variety of modalities and mutations, given that AI and ML models can potentially revolutionise mpox management. The imminent worldwide threat hence offers a chance to explore AI and ML approaches for implementation in research and healthcare to effectively mitigate the potential severity of the outbreak.