In Artificial Intelligence the black box refers to the fact that model insights are based on the dataset but the users are unaware of how they do and what they do. In this paper, we proposed the Model Explainability for Human- AI knowledge (MEHAK), Which is a significant model for COVID-19 detection that is a current and hot research topic in the field of medical image analysis by utilizing CT scan images. In this study, examine how a model classifies a disease in CT scan images when making a decision. Build a publicly available SARS- CoV-2 CT scan dataset, containing 1252 CT scans that are positive for SARS- CoV-2 infection (COVID-19) and 1230 CT scans for patients non-infected by SARS-CoV-2, 2482 CT scans in total. The LIME library is used to determine which aspects of the image have an effect on the predictions. Using the LimeimageExplainer function, the superpixels are sorted into groups based on the model and then visualized. we can see directly how the model decides on the predictions. The proposed approach beats the existing models with a score of accuracy of 93% precision of 90% and recall of 97% respectively.