The emergence of global health crises, notably the COVID-19 pandemic, has accentuated the critical importance of preventive measures, one of which is the wearing of face masks. Advanced face mask detection systems, utilizing innovative image processing techniques, have emerged as pivotal tools in disease prevention strategies. These systems not only enhance public health safety but also contribute to the efficient management of infectious diseases in various environments, from crowded urban settings to healthcare facilities. At the core of advanced face mask detection is the application of sophisticated computer vision algorithms, particularly deep learning models. These models are trained on vast datasets comprising images of individuals wearing masks, without masks, and in varying lighting and environmental conditions. By employing convolutional neural networks (CNNs), these systems can accurately identify and classify the presence or absence of face masks on individuals in real-time. This capability is crucial in environments where rapid decision-making is essential, such as airports, schools, and public transport systems. Moreover, the integration of image processing techniques not only facilitates mask detection but also enhances overall safety protocols. For instance, systems can be programmed to trigger alerts or record instances of non-compliance, thereby serving as both a deterrent and a monitoring tool. By automating compliance checks, organizations can allocate human resources more efficiently, allowing for a greater focus on other critical health safety measures, thus reinforcing the overall public health response. In addition to the identification of mask usage, advanced image processing systems can be adapted to evaluate other health parameters associated with the spread of infectious diseases. For example, analysing crowd density through video feeds could provide valuable insights into potential risk zones, thereby enabling proactive measures to alleviate viral transmission.