Maintaining physical separation between individuals continues to be an essential method for controlling the spread of infectious diseases like COVID-19, smallpox, the common cold, and malaria is through. Even though vaccination programs, especially for diseases like COVID-19, have dramatically reduced mortality rates, social distancing remains an important variable in reducing infection risks and preventing epidemic outbreaks. This paper brings out a cloud-driven real-time solution designed to track and enforce physical distancing across public spaces. Our system is built on the YOLO (You Only Look Once) framework for real-time human detection and powered by an advanced deep learning algorithm that calculates distances between entities in a 2D space. Hosting on cloud infrastructure allows for scalability and capability to process live video feeds from various cameras spread over different locations. Experimental results of the system show that human detection and distance calculation accuracies are more than 95%, and there is minimal delay due to this cloud-based setup. The results underscore that our method has the promise to be used for wide-scale, real-time surveillance of social distancing and brings in precious insights for public health management.