The problem regarding plastic waste requires ingenious solutions to make recycling more efficient and less environmentally harmful. Traditional methods of sorting plastic materials have several disadvantages; most rely on manual effort or low-degree automation regarding speed, accuracy, and scalability. Thus, this paper presents an innovative sorting system that employs IoT, generative AI, and machine learning to sort plastic waste in real-time. It manifests itself in a system where IoT sensors capture detailed waste characteristics, which later advanced AI algorithms sort into highly particularized categories of plastics. Generative AI models raise sorting accuracy by generating synthetic datasets to handle the variation within plastic waste forms, enhancing classification models’ robustness. Real-time data processing acts for immediate automated decision-making, thus considerably reducing the time taken in sorting and operational costs. The results indicated that the proposed system increases sorting efficiency by 30% compared to the traditional method and decreases processing time by up to 40%. This integrated approach now shows the possibility of IoT and AI technologies providing answers for reimagining waste management into a sustainable, circular economy. This work will provide a practical and workable model for deploying innovative waste sorting in industrial and municipal contexts, finding applications in addressing critical gaps existing within the current practices in the waste management sector.