The most common problems concerning smart city surveillance systems include data overload, inefficiency, and analysis. These challenges challenge the systems' ability to deal with issues related to crime, traffic congestion, or environmental factors in urban areas. This paper proposes a novel framework that integrates AI and IoT technologies to complement the surveillance of a smart city, including the analytical processing of data needed for real-time monitoring, detecting abnormal patterns, and deriving predictions from anomaly detection based on IoT-sensing devices deployed in all conceivable spaces in the city, sensing the views and dynamics around urban locales. The suggested framework demonstrated enhanced efficiency, accuracy, and decision-making capabilities in surveillance. AI analytics presented an opportunity to pinpoint possible threats through automated means, resource optimization, and pro-activity against urban challenges. Integrating AI and IoT in smart city surveillance provides a promising pathway for solving intricate problems in urban areas. Advanced technologies can enhance public safety, upgrade quality of life standards, and create more sustainable and resilient urban areas.