The proposed paper introduces a cutting-edge solution for enhancing surveillance in restricted public areas using a modern robotic approach. This robot is equipped with a wireless camera that can capture real-time footage during both day and night. The robot's design allows it to be easily controlled through a mobile app, leveraging an ESP Wifi module for seamless communication with an Android device. To enhance its capabilities, the robotic vehicle is integrated with a Machine Learning model through a Raspberry Pi for software processing. The Blynk App enables users to manually control the robot's movements based on commands received from the Android device, significantly reducing the need for human presence in hazardous environments that require continuous supervision and security. This system aims to autonomously identify various human activities through live video streaming, thanks to the integration of a machine learning model. The Android application not only facilitates remote control but also enables users to navigate the robot from a substantial distance using WIFI communication. Looking forward, the project holds promise for future advancements that could extend its applications to defense and mining areas. The robot is designed to distinguish between different types of human activities, monitoring live streaming information and transferring it to a connected Android device

Keywords: Surveillance, robot, spy, supervision
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