This interdisciplinary research project combines expertise in material science and machine learning to address the environmental challenges associated with metal extraction, specifically focusing on strategic metals like nickel, cobalt, and lithium. The team aims to develop nanocellulose/cyclodextrin-based hydrogels that efficiently and selectively capture these metals from aqueous solutions. The innovative approach involves leveraging artificial intelligence (AI) and machine learning (ML) models to simulate experimental data, significantly reducing time and resources required for optimization. The project's objectives include conducting a minimum viable experiment (MVE) to establish polymerization variables and performance evaluation methods. Machine learning algorithms, such as Variational Autoencoders and Generative Adversarial Networks, will be employed to generate additional data and optimize fabrication parameters. The team plans to optimize hydrogel performance in a single-metal environment, with the end goal of creating a versatile hydrogel capable of extracting various metal ions from contaminated solutions.