Dekka Satish , K. Narasimha Raju 1 1 (January - March) Effective SARS CoV 2 webbing allows for a speedy and accurate opinion of COVID 19 reducing the cargo on healthcare systems In order to estimate the threat of infection vaticination models that integrate numerous variables have been developed These are intended to prop medical help around the world in triaging patients particularly in areas where healthcare coffers are scarce We developed a machinelearning algorithm that was trained on the records of 51831people who had been tested of whom 4769 were verified to have COVID 19 The data in the test set came from the coming week tested individualities of whom 3624 were verified to have COVID 19 Overall we created a model that detects COVID 19 cases using simple variables available by asking introductory questions grounded on civil data intimately released by the Israeli Ministry of Health When testing coffers are limited our approach can be used to precedence testing for COVID 19 among other effects In this design we proposed the CNN grounded xray image forthe discovery of covid and xgboost for the discovery of symptoms Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026