Linked with environmental impact, rapid growth in the production of PE and PP makes searching for effective recycling solutions a matter of urgency. In traditional recycling, enormous challenges, contamination, degradation, and high-energy input make processes inefficient and not scalable. Generative AI can transform such challenges into optimized data-driven processes that enhance recycling outcomes. The paper reviews generative AI applications in advanced polyethylene (PE) and polypropylene (PP) recycling processes to improve process efficiency, quality control, and chemical recycling. This may allow high-purity sorting, predictive maintenance, and consistent chemical recycling due to generative AI over predictive modeling and real-time decision-making, turning plastic waste into reusable materials. However, generative AI implementation requires a significant amount of data infrastructure and also faces particular challenges because of data privacy concerns and compatibility with existing systems. Further, the paper discusses some environmental and ethical issues related to adopting AI-driven recycling and how such technology is envisaged to potentiate sustainable development. The review investigated recent developments and prospects that illustrated the potential of generative AI to be a game-changer in the recycling revolution regarding waste reduction, PE, PP, and circular economy objectives. In the final analysis, from the results obtained in this study, it seems that responsibly developed generative AI has quite a good chance of overhauling recycling regarding sustainable efficiency and the global imperative set by Earth.