The hydrometallurgical recycling of LIBs involves the dissolution of metallic components, primarily sourced from the active material (a mixture of cathodes and anodes). This dissolution is typically carried out using mineral acids, followed by metal separation techniques such as solvent extraction, ion exchange, and precipitation . Additionally, a thermal pre-treatment, involving pyrolysis or calcination, can be applied as well . Leaching serves as the initial step in the hydrometallurgy process, following mechanical or thermal pre-treatment. In this regard, only a few examples are found in the literature where ML techniques are applied to enhance the leaching process itself. Niu et al. proposal is one of these approaches . They utilized ML techniques to streamline the efficient leaching of metals from spent LIBs. They comprehensively analyzed all operational factors, including 20 input features related to the leaching process, encompassing both raw material properties and technological parameters. These factors were used to predict the output variables, namely the leaching efficiency of lithium, cobalt, manganese, and nickel. To gather data for ML analysis, the authors meticulously reviewed published references spanning from 2005 to 2022, accumulating a total of 17,588 data points related to the hydrometallurgy recycling of spent LIBs. To enhance prediction accuracy, they employed four ML algorithms: XGBoost (XGB), Random Forest (RF), Support Vector Machine (SVM), and AdaBoost. Subsequently, they developed and compared 16 models based on these algorithms. Building on the optimal models, the researchers designed a user-friendly graphical user interface (GUI) to aid researchers in swiftly obtaining metal leaching parameters from spent LIBs. This GUI only requires experimental measurements of particle size (screening) and waste feed composition, eliminating the need for extensive leaching experiments. Finally, the reliability of the GUI was verified through a series of experiments.