The industrial scaling of Green Hydrogen production is currently hindered by a significant lack of long-term operational failure data for Proton Exchange Membrane (PEM) electrolysers. To address this data gap and support thorough risk assessment, this research develops and validates an integrated, multi-layered reliability prediction framework for PEM electrolyser stacks. Unlike traditional statistical methods that need large historical datasets, this study employs a hybrid approach that combines top-down similarity analysis, fuzzy logic reasoning, and probabilistic modeling. The methodology is structured in three progressive phases. First, Rahimi’s similarity approach (adapted from subsea protocols) establishes baseline failure rates. Due to structural and electrochemical isomorphisms, the study utilized mature failure data from Proton Exchange Membrane Fuel Cells (PEMFCs) as a proxy for PEM electrolysers.