This paper deals with an automatic material handling system that coordinates the movement of a robotic arm to pick up items moving on conveyor belts. The system utilizes advanced sensors and machine learning algorithms to ensure precise and efficient manipulation of objects, enhancing the overall automation and productivity of material handling processes. It aims to organize colored objects approaching on the conveyor by picking and placing them in separate, designated locations. The robotic system employs advanced computer vision algorithms to precisely identify and manipulate the diverse array of colored objects, ensuring efficient and accurate sorting on the conveyor belt. This reduces the tedious work done by humans, ensuring accuracy and rapidity in the process. Additionally, it paves the way for more efficient utilization of human resources, allowing professionals to focus on higher-level tasks that require creativity and critical thinking. The system includes color sensors that detect the items' colors and transmit signals to the controller. Additionally, the controller processes the signals from the color sensors to facilitate accurate identification and sorting of the items. The microcontroller then guides the signal to the motor driving circuit, which operates the different motors of the robotic arm to grasp the object and place it in the correct location. Additionally, the robotic arm's sophisticated sensor feedback system ensures precise positioning and adaptability to varying environmental conditions, enhancing its overall efficiency in object manipulation tasks. Depending on the color sensed, the robotic arm goes to the correct location to release the object and returns to its normal position. Additionally, the robotic arm employs advanced computer vision algorithms to precisely identify and differentiate colors, ensuring accurate execution of tasks in diverse environments.