The pandemic of Corona Virus Disease is generating a public health emergency. Wearing a mask is one of the most efficient ways to combat the infection. This paper presents the detection of face masks, through mitigating, evaluating, preventing, and preparing actions regarding COVID-19. In this work, face mask identification is achieved using Machine Learning technique and the Image Classification algorithms are MobileNetV2 with major changes which includes Label Binarizer, ImageNet, and Binary Cross-Entropy. The methods involved in building the model are collecting the data, pre-processing, image generation, model construction, compilation, and finally testing. The proposed method can recognize people with and without masks. The training accuracy of the proposed method is 98.5% and the testing accuracy is 99%. This model is implemented in an image or video stream to detect faces with mask.