Abstract: Water molecules at solid-liquid interfaces play a pivotal role in governing interfacial phenomena that underpin electrochemical and catalytic processes. The organization and behavior of these interfacial water molecules can significantly influence the solvation of ions, the adsorption of reactants, and the kinetics of electrochemical reactions. The stepped structure of Pt surfaces can alter the properties of the interfacial water, thereby modulating the interfacial environment and the resulting surface reactivity. Revealing the in situ details of water structures at these stepped Pt/water interfaces is crucial for understanding the fundamental mechanisms that drive diverse applications in energy conversion and material science. In this work, we have developed a machine learning potential for the Pt(211)/water interface and performed machine learning molecular dynamics simulations. Our findings reveal distinct types of chemisorbed and physisorbed water molecules within the adsorbed layer. Importantly, we identified three unique water pairs that were not observed in the basal plane/water interfaces, which may serve as key precursors for water dissociation. These interfacial water structures contribute to the anisotropic dynamics of the adsorbed water layer. Our study provides molecular-level insights into the anisotropic nature of water behavior at stepped Pt/water interfaces, which can influence the reorientation and distribution of intermediates, molecules, and ions—crucial aspects for understanding electrochemical and catalytic processes.