Edge computing technology is changing rapidly as organizations try to use efficient and intelligent technologies for processing data. From the various literature obtained from recent articles, it can be found that some of the trends related to the future of edge computing are as follows. Edge AI is the term given for the application of artificial intelligence directly to edge devices like sensors, smartphones, cameras, etc., instead of the cloud. Defense surveillance systems need efficient detection of threats in real-time for the security of the nation. However, while using the cloud for surveillance systems, there are many problems related to connectivity. The paper proposes an Edge-Intelligence-enabled framework for the detection of events in real time from smart defense surveillance systems using IoT. Local processing of data from IoT sensors, cameras, and unmanned devices, integrating AI-driven analytics at the edge layer, enables faster detection of anomalies and intrusions and object tracking with reduced dependency on centralized cloud infrastructure. The proposed approach will enhance situational awareness, reduce network congestion, ensure data privacy, and guarantee operational resilience in dynamic and low-connectivity environments. Based on secondary data analysis, architectural design, key technologies, and implementation challenges of this study reveal the effectiveness of Edge Intelligence in strengthening next-generation defense surveillance systems.