The Corona Virus Disease popularised as COVID-19 is a highly transmissible viral infection and has severe impact on global health. It impacted the global economy also very badly. Ift positive cases can be detected early, this pandemic disease spread can be curtailed. Prediction of COVID-19 disease is advantageous to identify patients at a risk of health conditions. Applications of Artificial Intelligence (AI) techniques for COVID prediction from X-rays can be very useful, and can help to overcome the shortage of availability of doctors and physicians in remote places. This paper proposes a transfer learning model using Googlenet for COVID-19 prediction from chest X-ray images. For image classification we used GoogleNet which is one of the CNN architecture and is also named as InceptionV1. The positively classified images by our model indicate the presence of COVID-19. The results obtained in COVID prediction using GoogleNet with a training accuracy of 99% and testing accuracy of 98.5% emphasize the use of Transfer Learning models in disease prediction. Keywords-X-ray images of chest, Prediction, COVID-19, GoogleNet. 1 INTRODUCTION THE 1 CoronaVirus Disease (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) and is highly transmissible. It came into China government's notice in December, 2019 in Wuhan and more than twenty five million people all over the world were affected by it. Coronavirus is challenging all the people and the technology on the entire planet. As of August 2020, there are more than 27 million COVID-19 cases and 873,000 deaths globally [1]. There's no vaccine or immunizing agent found till date, thus the challenge is how best to fight against the Coronavirus to prevent its transmission. People with low immunity, old age, and medical issues especially associated with lungs are more vulnerable to COVID-19 sickness. The symptoms of COVID-19 are cough, cold, high fever and respiration issues. Preventive measures for COVID-19 square