Monkeypox (Mpox) is a zoonotic virus resembling smallpox but with lower mortality. Once endemic to West and Central Africa, Mpox has re-emerged globally, prompting the World Health Organisation (WHO) to declare it a Public Health Emergency of International Concern. The cessation of smallpox vaccination has contributed to its increased clinical relevance. To address the growing need for real-time monitoring and rapid response, this paper proposes an Internet of Things (IoT) based predictive analytics framework that integrates machine learning to forecast outbreaks and optimize healthcare resource allocation. IoT devices enable real-time collection of environmental, physiological, and social data, which are analyzed using machine learning models. This study evaluates the predictive performance of Random Forest and Support Vector Machine (SVM) algorithms applied to IoT-generated datasets. The framework also incorporates time series analysis using ARIMA and SARIMA models to understand the trends of outbreaks. Experimental results demonstrate that the integrated IoT-ML approach improves forecasting accuracy and enhances timely decision-making in epidemic response. The findings highlight the potential of combining IoT and machine learning technologies for proactive monkeypox surveillance and efficient public health resource deployment.