This paper proposes a smart recycling bin that aims to increase the recycled waste collected from homes and eventually reduce the amount of resources needed to carry out the process at a later stage. The bin is mounted on a servo motor and is divided into three sections for general, paper, and plastic types of waste. An ultrasonic sensor detects the user approaching and prompts a camera to capture multiple frames. The smart bin is powered with artificial intelligence and computer vision to detect the type of waste the user intends to throw. This data is transmitted to a Raspberry Pi, which subsequently utilizes the received information to adjust the position of a servo motor, thereby aligning the bin with the relevant waste category. The bin communicates with the AI Recycler App through Firebase. The app’s objectives are to provide information to the user about the CO2 emissions prevented due to their actions and monitor their behavior through points of encouragement. The bin is 3D printed using material collected from recycled plastic bottles. We use the YOLOv8 model for waste detection. The model is trained and tested on the TrashNet dataset with an mAP of 98.9% and an accuracy of 95%, which showed superiority over other state-of-the-art implementations.