@article{article_1819863, title={Integrated Smart Waste Management System Based on Artificial Intelligence and IoT}, journal={Innovative Artificial Intelligence}, volume={1}, pages={18–25}, year={2025}, author={Suncak, Atilla and Akkoç, Oğuzhan and Çeykel, Hüseyin Eren and Üçgül, Fatma Zehra}, keywords={Artificial intelligence, Deep learning, IoT sensor, Smart Waste Management, Sustainability}, abstract={This study presents an innovative smart waste management system developed through the integrated use of artificial intelligence, IoT and mobile technologies. The system features a multi-layered architecture that combines sensor-based data collection, real-time monitoring via a cloud infrastructure, AI-assisted waste classification and user interaction. Container occupancy rates were measured using an ESP32 microcontroller and ultrasonic sensors, and the data was transferred to the Firebase infrastructure. In the image classification process, the CNN model achieved 94.5% accuracy, while the Gemini model, which requires no additional training, demonstrated superior performance with 97.1% accuracy. The mobile application increased users’ recycling awareness, while the web-based panel provided occupancy tracking and route optimization for managers. The results demonstrate that the hybrid CNN-Gemini approach increases waste classification accuracy and system efficiency. This holistic structure is considered a low-cost, scalable and environmentally friendly solution for sustainable smart city applications.}, number={2}, publisher={Dokuz Eylul University}