As urban areas expand and interconnectivity grows, public safety in smart cities becomes paramount. To address this, integrating AI-assisted IoT systems presents a transformative approach to enhancing public safety. This study proposes a comprehensive safety framework tailored for smart cities that leverages A IoT technology, seamlessly combining various sensors—such as eye blink, ultrasonic, and alcohol sensors—to ensure road and public safety. The eye blink sensor detects driver fatigue, issuing immediate auditory alerts to prevent potential hazards, thus enhancing real-time safety. Ultrasonic sensors deliver continuous data on the speed and proximity of nearby vehicles, optimizing traffic flow and reducing the likelihood of collisions. Addressing the risks associated with alcohol impairment, our framework incorporates an alcohol detection sensor, which, upon identifying high intoxication levels, utilizes GPS and GSM technologies to automatically adjust vehicle speed and inform relevant authorities, enabling prompt intervention. Additionally, the system incorporates Li-Fi technology to improve inter-vehicle communication through Visible Light Communication (VLC), facilitating rapid data exchange across connected vehicles. This Artificial Intelligence of Things (AIoT) enabled framework provides a foundation for safer and more cohesive urban transportation, contributing to a smarter, sustainable, well-integrated public safety network in smart cities. Our model paves the way for significant improvements in urban safety infrastructure through these advancements, setting a standard for evolving smart city safety solutions.