Ensuring security in critical infrastructures, industrial facilities, and remote areas presents a persistent challenge. Traditional methods, such as fixed surveillance cameras and human patrols, suffer from inherent limitations in coverage, reaction time, and analytical capabilities. Human guards are prone to fatigue, errors, and cognitive biases, while static cameras lack mobility and adaptability. To address these constraints, this thesis presents the development of an autonomous robotic patrolling system that integrates artificial intelligence, real-time data streaming, and mobile robotics to enhance surveillance efficiency. The proposed solution is designed to be platform-agnostic, allowing the robotic base to be interchangeable without modifying the perception and processing pipeline.