This paper introduces an advanced smart city surveillance systems for the urban US environment using Artificial Intelligence (AI) and the Internet of Things (IoT) from AIoT and the encrypted data communication for urban safety and security. The system developed in this study can analyze real-time data from IoT devices (e.g., surveillance cameras and environmental sensors) to determine events such as criminal issues, accidents, or disasters with independent decisions. Such incidents are immediately reported by AI-generated incident summaries over encrypted channels to the appropriate authorities guaranteeing the security of data. A significant result of this study is lower times of responses in seconds in incident detection and reporting. A detection accuracy that is 25% higher than conventional methods is shown and robust data security is guaranteed. By offering proactive, real-time security management, this surveillance system adds to a safer urban climate and promotes public confidence in smart city solutions. The report also considers issues such as the cost of implementation, the privacy implications and the data requirements, and offers a scalable parking management solution to U.S. cities. The research also offers a glimpse of the system's scalability, the ability to embed into the existing city dynamic, and to grow on a broader scale on urban environment. This paper discusses the state-of-surveillance in the future, and what technologies from state-of-the-art like AI and better encryption method used can further improve the reliability of the system, The proposed work provides a complete framework of how cities in the U.S. can think about safety in the digital age.