The widespread adoption of plastics attributed to their cost effectiveness, durability, lightweight nature and versatility, poses significant challenges in achieving sustainability due to their improper disposal. Given the pervasive use of plastics in every aspect of daily life, recycling emerges as a potential solution, yet with numerous recycling methods available, selecting the most suitable option for sustainable and green operational systems remains a formidable task. Approaching this issue, the integration of artificial intelligence (AI) with the advanced fuzzy logic methods to prioritize in plastic waste recycling (PWR) strategies offers a promising pathway toward sustainable development goals. This paper explores an innovative framework for evaluating and prioritizing PWR methods, integrating an AI model (YOLO v8) into the sorting process of highest ranked recycling method, in combination with Pythagorean Fuzzy Sets (PFS). The proposed approach leverages TOPSIS to rank PWR methods, effectively addressing uncertainty and imperfection in the decision-making processes. The results showed that Mechanical recycling ranked highest for its sustainable and green method, while Biological recycling was least ranked among PWR alternatives with the lowest entropy scores of 0.673 for circular economy factor. Finally, sensitivity analysis was conducted by varying weights of sustainable and green operational systems, thus confirming the coherent performance of the proposed method under changing conditions.