Proton-exchange Membrane (PEM) fuel cells are essential systems for hydrogen-electric powertrains in aviation, aiming to meet climate-neutral goals. However, their integration faces challenges, particularly regarding power density, reliability, and durability. This research addresses PEM fuel cell durability through Prognostics and Health Management to predict Remaining Useful Life (RUL) under static and quasi-dynamic conditions. We propose a prognostic method utilising Echo State Networks (ESNs) to manage the chaotic time-series data of PEM fuel cells, extending the Prognostic Horizon (PH) to 125 hours. Our approach involves decomposing stack voltage and current time-series data into trend, seasonal, and residual components via Seasonal and Trend decomposition using LOESS, and predicting these iteratively with ESNs optimised through Bayesian optimisation using Optuna. A comparative study found that ESNs perform best at predicting trends in single-input, single-output forecasts of current time-series, while Long Short-Term Memory networks are better at capturing seasonality and residuals. Additionally, while empirical and semi-empirical models assessed PEM fuel cell membrane health, their effectiveness in predicting RUL in combination with predicted stack voltage was limited by average degradation across cells. This study presents a robust and universal prognostic approach for PEM fuel cells, facilitating their reliable integration into aviation applications.