ML-based simulations have been shown to be helpful in predicting intraoperative outcomes in order to enhance judgment on the course of action. ML strategies are being used in this research to predict future COVID cases, and they are being evaluated to identify whichever algorithm is most appropriate for the COVID sample. This research confirms how ML algorithms can anticipate the proportion of upcoming Covid-19 individuals who will be harmed. Forecasting models such as LR, Random Forest, SVR, KNN, DT, and Elastic net were used to make the projections. For the upcoming 10 days, the number of newly infected cases is predicted by each model. The consequences display that the Decision Tree achieves top amongst those simulations, trailed by Linear Regression and K-NN, which are good at predicting new confirmed cases. Whereas SVR performs worst among those models using the dataset that is currently available.