Campus energy management faces continuous pressure to enhance sustainability while maintaining public safety standards. Traditional methods for controlling streetlights rely heavily on timer-based schedules or passive sensors, which are not only rigid in adaptability but also inefficient in energy utilization during low-traffic periods. To address this challenge, this paper proposes a Data-Driven Smart Streetlight framework that integrates Artificial Intelligence (AI) vision and Internet of Things (IoT) technologies. The framework comprises a hybrid edge-cloud architecture: at the edge, a Raspberry Pi-based platform uti-lizes YOLO object detection to fuse heterogeneous sensor data (LDR, PIR), en-abling precise situational awareness. Specifically, a multi-modal brightness con-trol algorithm is employed to dynamically adjust illumination based on real-time pedestrian density, incorporating a temporal smoothing mechanism to mitigate abrupt flickering. The core functionalities regarding data transmission are opti-mized using lightweight MQTT and HTTP MJPEG protocols, and visualized through a unified ELK stack interface, enabling administrators to intuitively ac-cess management data. Experimental results demonstrate that the system achieves a low end-to-end latency of approximately 104 ms and significantly re-duces energy consumption by up to 66% in low-traffic scenarios compared to conventional lighting.