Public spitting in cities has become a significant sanitation and public health problem, especially in crowded areas. Spitting gutka and tobacco damages public facilities, raises cleaning costs, spreads diseases, and harms urban hygiene. Current sanitation systems mainly focus on cleaning after the fact instead of preventing issues and using smart monitoring. This research suggests an AI-Driven Smart Public Hygiene Enforcement and Automated Waste Disposal System. It combines computer vision, IoT monitoring, automated alerts, embedded systems, and sanitation infrastructure into a single framework for smart cities. The system uses cameras and edge AI processors, like Raspberry Pi and Jetson Nano, to detect spitting behavior in real time with OpenCV and YOLOv8-based activity recognition models. When the system detects a violation, it automatically activates visual and audio alerts. At the same time, it triggers an automated disposal and flushing mechanism. The event data is recorded for later analysis, maintenance planning, and integration into smart city systems. This framework lessens the need for manual cleaning, cuts maintenance costs, raises public awareness, and supports better urban hygiene management. The research shows that combining AI surveillance with automated cleaning technology can create scalable public hygiene systems for future smart cities.

Keywords: Public Hygiene; Smart City; AI Surveillance; Computer Vision; IoT, Smart Sanitation; Urban Infrastructure; Edge AI; Waste Disposal Automation;