A Comprehensive Review of Machine Learning Applications for Advancing Reliability and Safety: A Multi‐Method Survey of Models and Techniques

ABSTRACT Machine learning (ML) has become universal across a growing number of academic fields and sectors, driving fundamental changes in areas such as autonomy and computer vision. Reliability engineering and safety are also expected to undergo significant transformations through the application of ML techniques. However, the existing body of literature on the use of ML in these domains remains extensive but fragmented, posing challenges for synthesis into a unified framework. This study aims to address these challenges by presenting a comprehensive review and guide for this evolving field, highlighting key milestones, models, and pathways. Initially, we provide an overview of various ML techniques and their application in reliability and safety, showcasing key models and algorithms. We then retrospectively examine the use of ML in these contexts, with special attention to the growing prominence and unique benefits of deep learning techniques. Finally, we project future opportunities for ML in advancing reliability and safety, emphasizing the potential to enhance decision‐making processes and improve accident prevention measures through more precise data analysis.