The Face Mask Detection model is used to make sure a person is wearing a mask or not. This model results from the grappling situation presented by COVID-19, resulting in the mandatory use of masks at public places. Security agencies need to plant actual personnel to make sure all the people in public are wearing ‘masks’, this model will lessen the risk of people being contacted by COVID-19. This research helps us understand a broader perspective about the Face Mask Detection models by comparing different state of the art models. The model uses MobileNetV2 architecture that has inverted bottlenecks and depth-wise convolutions to filter features. The complete model is built in two phases, the first one consisting of making a Face Mask Detection model trained to detect the face and mask, and then placing it in the Real Time environment by using the OpenCV for actually predicting the usage of face mask.