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This document is a compilation of various research papers and articles that discuss the use of machine learning and other techniques to predict fatigue lifetimes in different materials and structures. The papers cover topics such as the use of neural networks, XGBoost, and physics-informed machine learning models for fatigue prediction. The research focuses on different materials, including aluminum alloys and lead-free solders, and explores the effects of factors such as wear, lubrication, corrosion, and stress on fatigue life. The papers provide insights into the application of machine learning techniques in predicting fatigue lifetime and offer potential solutions for improving the accuracy of these predictions. [Extracted from the article]