This paper explores the application of machine learning (ML) techniques in predicting fatigue life of metals, a critical aspect of structural integrity in various engineering fields. Initially, the paper thoroughly explores factors influencing fatigue life, including material properties, manufacturing processes, operational conditions, environmental considerations, and usage factors. Additionally, it includes a case study on the fatigue behavior of High-Mn TWIP steels, highlighting how factors like grain size, persistent slip bands, and processing techniques impact their performance and guide optimization for automotive applications. Besides, it emphasizes the potential of ML in enhancing prediction accuracy, particularly through the integration of physics-based models with data-driven approaches. This study provides valuable insights into the current state and future prospects of ML-driven fatigue life prediction, highlighting its potential to revolutionize structural health monitoring and maintenance strategies across industries such as aerospace, automotive, and civil engineering.