The COVID-19 has become a global pandemic, which has already affected the health care and lifestyle of literally every person on earth. It has affected all over the world. The global healthcare system is being overwhelmed by the exponential increase in COVID-19 patients. One of the critical factors driving the rapid spread of the COVID-19 pandemic is the lengthy clinical testing time. In order speed up the process the identification process can be done using chest X-Ray. In this project, a computer vision-based deep learning model for detecting Covid-19 is proposed. A dataset scraped from the internet was used to train the model. NumPy, OpenCV, Matplotlib, and TensorFlow are among the Python 3 packages that are used to create the computer vision model. This method is created by training and testing the model to identify failures and establish recovery strategies. As a result, early diagnosis of Covid-19 using computer vision-based approaches is employed to lower the mortality rate. The work suggests using Convolutional Neural Networks (CNNs) to detect Covid-19. The model is fed data from chest X-ray images. The neural network is trained using a dataset of classified X-Ray images and classified between normal image and covid-19 image. It approaches the problem as a binary classification problem, that is, either Covid-19 or not. The goal is to achieve high accuracy and detect Covid-19 using the defined training model.