Proton exchange membrane fuel cells have grown as a future technology for renewable energy conversion, utilizing hydrogen and oxygen to generate electricity with water as a single byproduct. Nafion 112 membrane, known for its superior ionic conductivity and mechanical stability, is important for the efficiency of PEMFCs. However, the complex interactions within the fuel cell environment, particularly concerning water management, present significant challenges that can affect performance and longevity. This paper utilizes ML techniques to visualize and analyze Nafion 112 membrane behavior in PEMFCs. By integrating advanced imaging methods, with ML techniques, we aim providing a comprehensive understanding of the fluid dynamics and phase interactions occurring within the fuel cell. The proposed approach facilitates detailed segmentation of the membrane along with surrounding components, allowing for enhanced modeling of water transport mechanisms. The paper gives a clear idea of Nafion 112 membrane behavior under various operational conditions and also paves the way for optimizing PEMFC designs. By using machine learning for visualization and analysis, we anticipate significant advancements in accomplishing durability of PEMFCs, ultimately supporting the transition to sustainable energy solutions.