The COVID-19 pandemic has made face mask detection into a big thing because it is essential in public health monitoring. Meanwhile, the growing number of things that can be connected to the internet and the increasing integration of this technology mean that edge devices are now in demand for effective real-time face mask detection models. Often, existing methods require some kind of pre-installed equipment or difficult-to-manipulate environmental conditions, and computational resource constraints essentially put an end to them. In the present study, a hybrid Flame-Sailfish Optimization (HFSO)-based deep learning framework is proposed. It combines the feature extraction capabilities of ResNet50 with the efficiency of MobileNetV2. The HFSO algorithm optimizes crucial parameters such as detection thresholds and learning rates. So that the model can take full advantage of computing capacity and still operate in real time on devices with limited resources. The model was tested on three data sets - Kaggle Face Mask Detection dataset, Public Places dataset, and Public Videos dataset - achieving up to 97.5% accuracy. It outperformed the previous leader in all cases. The results prove that this framework is reliable and easily applicable for identifying people wearing masks under different conditions. However, where there is great occlusion of the face or video feed quality is bad, the model's performance will drop somewhat. Future work should focus on increasing difficulty in detections, broadening the application of this method to other health monitoring systems based on the Internet of Things, and ensuring that its robustness remains unaltered.