The corona, often identified as SARS-CoV-2, has ravaged large sections of the globe, and the situation is bad. It is a kind of pandemic sickness that is distribution from individual to individual on a daily basis. It is critical to have pathway of the amount of patients that are afflicted as a result. Due to the present manner of electronic data collecting, it is difficult to evaluate and anticipate disease transmission both locally and worldwide. To get around this problem, machine learning methods might be employed to effectively map the illness and its evolution. Deep learning, a part of computer technology, is critical for accurately identifying persons with the illness by analysing chest X-ray pictures. Controlled artificial intelligence models with connected algorithms (such as LR, SVR, and Time series algorithms) for information analysis for regression and classification are useful for teaching the model to forecast the total amount of confirmed patients worldwide who will be susceptible to the illness in the coming days. Once the global dataset has been gathered, pre-processed, and retrieved, the number of verified occurrences up to a given date is acquired and used as the training set for the model in this proposed work. To anticipate the rise in occurrences over the following several days, the model is built using supervised machine learning methods. a schedule Holt's classical outperforms regression of linear and vector of support machine in the experimental situation when using the aforementioned methodologies.