With the Covid-19 virus, Indonesia has a high risk of being exposed to the Covid-19 virus. Detecting Covid-19 can be done with medical imaging tools, one of which is a CT-Scan of the lungs through an intelligent system. In this research, an intelligent system is designed using Euclidean Distance and Manhattan Distance. The purpose of this study was to determine the best accuracy results from the comparison between Euclidean distance and Manhattan distance. The data set used is 349 CT-Scan images of Covid-19 lungs and 397 CT-Scan images of Non-Covid-19 lungs. In this study, 3 scenarios were tested. The method used is to perform the initial preprocessing stage by changing the image size and converting the image into grayscale form. Then the distance between pixels is calculated and the closest value is searched to obtain the results. The results obtained from this study were based on a trial of 3 scenarios using Euclidean Distance and Manhattan Distance, the best results were obtained in the 3rd scenario. In the third scenario using the Euclidean Distance, the accuracy is 82.87%, precision is 76.08%, and recall is 85.71%, while using the Manhattan distance, the accuracy is 86.98%, precision is 77.77%, and recall is 85. ,71%. So in this study it can be concluded that the best accuracy results are using the Manhattan Distance with an accuracy value of 86.98%, precision 77.77%, and recall 85.71%.