Yi Zhou, Handong Jiao, Xin Lu, Dongbai Sun, Shaolong Li, Jianxun Song, Wei Wang, Shuqiang Jiao
ABSTRACT Metal electrodeposition is a common electrochemical process widely used in various industries, including energy engineering, chemical engineering, metallurgy, manufacturing, and surface engineering. During electrodeposition, the morphology is governed by the multiple coupling effects of multivariate, multidimensional, multi‐spatial‐scale, and multi‐time‐scale mechanisms. Current research on electrodeposition morphology has made significant progress. However, the lack of a systematic review limits efficient guidance and hinders the accurate design of metal electrodeposition morphology. Consequently, this review comprehensively explores the formation mechanisms of electrodeposited morphologies under key coupling effects from a perspective of multivariate, multidimensional, multi‐spatial‐scale, and multi‐time‐scale interactions. It also proposes corresponding regulation strategies in accordance with the required morphology. Furthermore, addressing the bottlenecks of numerous influencing factors and massive data volumes, it presents an innovative paradigm that leverages in situ characterization and theoretical calculation data, as well as integrates machine learning and digital twin technologies to establish predictive models for electrodeposition morphology and achieve real‐time control over the deposition process. This review provides mechanistic insights and strategic guidance for metal electrodeposition morphology optimization, advances future engineering practices, and achieves efficient, collaborative, and intelligent advancements in metal electrodeposition.