ABSTRACT The rapid evolution of the electric vehicle (EV) industry demands advanced materials for battery protection, with polyurethane (PUR) foams emerging as a promising solution due to their thermal insulation, mechanical adaptability, and fire resistance properties. This study introduces an active learning‐driven inverse design (AL‐ID) framework, leveraging machine learning (ML) to systematically optimize PUR foam compositions exhibiting desired density and mechanical strength. AL was employed to iteratively r…