The electrification of society is an essential component of the effort to achieve a fossil-free world, where solar cells will play a central role in the future energy system. The advancement of photovoltaics should be aligned with principles of the circular economy. In the last years, lead halide perovskites have risen as a leading candidate for third-generation solar cells, experiencing rapid advancement. However, the manufacturing of commercial products inevitably generates significant waste and end-of-life devices, leading to potentially severe environmental repercussions. To tackle this challenge, proactive research and development of recycling and recovery technologies for perovskite solar cells are very necessary. Here we introduce a proof of concept, which, to the best of our knowledge, is the first AI-guided method designed to predict the optimal recycling treatment for perovskite solar cells and the first construction of a perovskite recycling dataset. Using sentiment analysis language processing for the first time, we achieved up to 70% accuracy in predicting the correct action for a given device structure. This innovative approach opens up new possibilities for applying sentiment analysis as a tool for e-waste recycling.