Electric cars, renewable energy storage systems, and portable devices all depend on lithium-ion batteries. Concerns concerning efficient end-of-life management and recycling are brought up by the demand spike, nevertheless. This research investigates how to best recycle lithium-ion batteries by combining Explainable AI with transportation mode analysis. In this research, different machine learning models are explored for profit prediction in recycling operations including techniques like data acquisition, data processing, model selection, and Explainable AI frameworks. The most accurate model for predicting profits is the ensemble model, which combines the regressors from Random Forest and Extra Trees. It obtains the lowest MAE of 0.01, suggesting minimum variance between expected and actual earnings. The examination of transportation characteristics also sheds light on the best places for recycling and modes of transportation. Our findings reveal that combining ensemble model significantly improves profit prediction accuracy for lithium-ion battery recycling, with LIME identifying “Type” and “Cathode Scenario” as key predictive factors. The amalgamation of transportation mode analysis with Explainable AI offers a propitious methodology for optimizing the recycling of lithium-ion batteries, hence furnishing discernible insights into the decision-making process and augmenting operational efficiency.