The increasing number of Monkeypox (Mpox) cases in non-endemic countries resulted in the WHO declaring a public health emergency of international concern. Accurate and timely diagnosis of Mpox has a critical role in containing the spread of infection. Diagnosis currently relies on PCR, which requires trained personnel and complex laboratory infrastructure. Thus, the development of point-of-care (POC) tools are essential to facilitate rapid, accurate, and user-friendly diagnosis. Here, we review POC diagnostic tools available for Mpox. We also discuss bottlenecks preventing the widespread implementation of POC platforms for Mpox diagnosis and potential strategies to address these limitations. Furthermore, we describe future directions, including the role of machine learning (ML) and deep learning (DL)-based models and the integration of integrated field-deployable platforms for Mpox diagnosis.