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The biosphere has been exaggerated adversely with the emerging of the COVID-19 at the end of 2019. It is anticipated that the present pandemic will be tackle with precautionary steps since there is no reliable vaccine that can be used to combat the outbreak globally. Hence, the newly emerging technologies will have noticeable roles to play during this pandemic. Therefore, this chapter proposes real-time diagnosis system to combat the spread of COVID-19 outbreak. A Cloud-IoMT-based framework was developed to collect real-time data for early diagnosing patients in real time. Four distinct machine learning: Extra Trees, Random Forest (RF), XGBoost, and Light Gradient Boosting Machine (LGBM) were used for quick and better identification of potential COVID-19 cases. The dataset used contains COVID-19 symptoms and selects the relevant symptoms of the diagnosis of a suspect person. The results …