In an era of rapid urbanization, ensuring public safety has emerged as a paramount concern for governments and municipalities worldwide. Traditional urban surveillance methods, though somewhat effective, often fall short in addressing evolving challenges. This paper introduces a groundbreaking solution by harnessing the synergy of drones and artificial intelligence vision systems. It presents the Smart Drone Surveillance System (SDSS), a comprehensive monitoring solution integrating drones, AI vision systems, and IoT protocols to enhance urban security and response efficiency by integrating necessary traditional systems such as fire system, transport system etc. The system is adept at detecting traffic congestions and fire accidents, which is crucial for urban environments. The SDSS is very good at finding and keeping an eye on possible deviations because it uses advanced algorithms like YOLOv8 for object detection and Proportional-Integral-Derivative (PID) controller-based algorithms for controlling drone flight. Our methodology underscores the pivotal role of simulation in system development, ensuring thorough testing and refinement before real-world deployment. We validate algorithms through simulations using Gazebo to foster safety and scalability without the constraints of physical hardware limitations. Furthermore, the system’s geo location and mapping capabilities, powered by ORB feature detection and GDAL, enable precise anomaly localization and visualization for comprehensive analysis. The seamless integration of ROS, Gazebo, and QGIS forms a robust tech stack, facilitating system development, simulation, and visualization. This paper represents a significant advancement in urban surveillance technology, promising heightened security and safety through the fusion of drones, AI, and IoT.