question: System-based prediction based on machine learning has demonstrated its utility in predicting the appropriate outcomes for the longer-term course of activities. There are numerous applications in which machine learning models are used to identify and prioritize unfavorable factors for threats. There have been a lot of deaths in various parts of the world caused by the Novel Coronavirus or COVID-19. There is no doubt that the best pandemics in world history are grouped together in this warning to public health. Increasing the accuracy of our predictions requires identifying the characteristics that could show correlation with COVID-19's spread. The purpose of this article is to deliver an import an ten hanced consideration of how machine learning models are frequently used in real-life settings. To predict COVID-19 and to forecast the threatening variables of COVID-19, we compared four machine learning models: Linear Regression model, Polynomial Regression model, Support Vector Machine (SVM) model, and Bayesian Ridge Polynomial Regression model. Based on the models, a forecast of confirmed cases over the next ten days can be made. In addition to analyzing the present pattern or trend of COVID-19 spread, this paper also defines the challenges for future research. © 2022 IEEE.