The importance of mask compliance in stopping the transmission of airborne illnesses was brought to light by the COVID-19 pandemic, especially in high-risk settings like hospitals and intensive care units (ICUs). Manual enforcement of mask-wearing policies is still ineffective and subject to human error, even with stringent regulations. Ensuring real-time compliance is crucial to lowering infection rates and safeguarding healthcare workers in nations like India, where a large patient intake and a shortage of medical staff pose extra difficulties. Even uneven compliance with mask laws worldwide still impacts public health, necessitating automated solutions. We suggest Ward Guard, an artificial intelligence (AI) powered real-time mask detection system intended for healthcare facilities, to solve these issues. The proposed MobileNetV2-base method achieved an accuracy of 93.5%. It recognizes people who are not wearing masks using computer vision and deep learning, and it promptly alerts specified medical staff through Twilio-based messaging. In contrast to conventional surveillance, Ward Guard improves hospital safety by automating compliance monitoring, drastically lowering the need for physical intervention. The system is perfect for implementation in urban and rural healthcare settings since it is scalable, flexible, and can function in contexts with limited resources. The Ward Guard uses MobileNetV2, which is tailored for edge devices like the Raspberry Pi and NVIDIA Jetson. Hospital administrators may efficiently enforce policies by using the system’s integration of cloud-based analytics to track compliance patterns over time. The suggested approach ensures low latency and real-time processing, guaranteeing a prompt reaction to non-compliance. Ward Guard is a next-generation hospital safety technology that will be enhanced with voice-based warnings, broader PPE detection (gloves, face shields), and connectivity with Electronic Health Records (EHR) for automated reporting.