This paper provides a systematic review of the fatigue life prediction of plain concrete, emphasizing the utilization of machine learning (ML) algorithms, finite element method (FEM) simulations, and comprehensive statistical analyzes. In fatigue assessment, various influential parameters such as frequency, stress conditions, and aggregate types can significantly impact the results. Therefore, it is crucial to thoroughly investigate these parameters to achieve a comprehensive understanding of the behavior and properties of concrete under fatigue conditions. Conventional fatigue testing methods often face challenges related to extensive testing time and high costs, which can be effectively mitigated through the implementation of FEM simulations. FEM simulations have proven to significantly reduce fatigue testing time while maintaining high accuracy and reliability of the results. Moreover, it is essential to analyze the probability distribution of fatigue data to gain insights into its underlying patterns and variability. Various probability distributions, such as Weibull and lognormal distributions, have been utilized to model fatigue data effectively. Studying different distributions is necessary, as each fitted distribution can yield distinct results based on the specific parameters under investigation. Fatigue life prediction can be enhanced using numerous ML models including artificial neural networks (ANN), random forests (RF), support vector regression (SVR), and decision trees (DT), which offer superior capabilities in handling the complex relationships between multiple influential parameters compared to traditional mathematical methods. This research aims to enhance the understanding of concrete behavior and properties under fatigue and to provide alternative, technologically advanced methods for predicting fatigue life.