Airborne illnesses like chickenpox, influenza, and COVID-19 pose a major risk to public health since COVID-19 has killed about 7 million people. Wearing face masks has therefore become mandatory and significant. in order to prevent the spread of certain illnesses, particularly in healthcare institutions such as hospitals. This study introduces a scalable deep convolutional neural network (DCNN)-based face mask monitoring system that is better than manual surveillance, particularly in high-density settings. This study offers three methods: First, the pre-trained algorithms model, which included seven different algorithms and was optimized with hyperparameters to find optimal settings; the Darknet-53 algorithm performed the best among them, achieving an accuracy of 97.5%. The second was a customized DCNN model that achieved 96.4% accuracy in binary mask detection. The last suggested system is a hybrid model that improves the accuracy and stability of the model by using pre-trained algorithms as classifiers and a DCNN as a feature extractor. AlexNet and Darknet-53 were tested as classifiers in our study; Darknet-53's accuracy was 98%.