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Applications of Structural Health Monitoring (SHM) combined with MachineLearning (ML) techniques enhance real-time performance tracking and increasestructural integrity awareness of civil, aerospace and automotiveinfrastructures. This SHM-ML synergy has gained popularity in the last yearsthanks to the anticipation of maintenance provided by arising ML algorithms andtheir ability of handling large quantities of data and considering theirinfluence in the problem.  In this paper we develop a novel ML nearest-neighbors-alike algorithm basedon the principle of maximum entropy to predict fatigue damage (Palmgren-Minerindex) in composite materials by processing the signals of Lamb Waves -- anon-destructive SHM technique -- with other meaningful features such as layupparameters and stiffness matrices calculated from the Classical Laminate Theory(CLT). The full data analysis cycle is applied to a dataset of delaminationexperiments in composites. The predictions achieve a good level of accuracy,similar to other ML algorithms, e.g. Neural Networks or Gradient-Boosted Trees,and computation times are of the same order of magnitude.  The key advantages of our proposal are: (1) The automatic determination ofall the parameters involved in the prediction, so no hyperparameters have to beset beforehand, which saves time devoted to hypertuning the model and alsorepresents an advantage for autonomous, self-supervised SHM. (2) No training isrequired, which, in an \textit{online learning} context where streams of dataare fed continuously to the model, avoids repeated training -- essential forreliable real-time, continuous monitoring.

Self-learning locally-optimal hypertuning using maximum entropy, and  comparison of machine learning approaches for estimating fatigue life in  composite materials