Fault tree analysis (FTA) is one of the most popular failure analysis techniques that reveal the potential pathways leading to systems or components failure. It has been widely employed in numerous sectors to understand how a system fails and is improved. However, the conventional FTA has been criticized due to a series of inherent shortcomings in the FTA state of the arts. Accordingly, scholars, engineers, and practitioners made their attempts to improve the FTA by dealing with its critical deficiencies over the last decade. However, a few works have been performed to review and synthesize the relevant studies on FTA improvement topics. Thus, the present study is aimed to carry out a systematic literature review of the state‐of‐the‐art theoretical and empirical findings concerning FTA improvement from 2011 to 2021 using the Scopus database collection. In this sense, an in‐depth investigation is conducted using statistical metadata analysis. This subject discusses frontier directions and development trends to reveal the research status. In addition, a bibliometric study is undertaken to ascertain the most productive and influential researchers, research centers, and hotspot fields. It also sheds light on the FTA shortcomings in the existing literature, the evolution in FTA improvement topics, and research opportunities. The outcomes of the present work highlighted that the annual publications on FTA improvement topics are significantly growing, especially after 2019. Besides, Jianxiu Wang, Yan‐Feng Li, and Yihuan Wang are the most productive, prolific, and highly cited authors worldwide; and Asia, particularly China, is the leading contributor in the FTA area. According to Bradford's law, one‐third of all publications (7995) in the field of FTA improvement have been published by 40 sources. Finally, “Decision‐making,” “Risk analysis,” “Uncertainty Analysis,” and “Bayesian Networks” are the four major hot topics integrated into improving the conventional FTA.

This study presents a responsive analysis of the role of artificial intelligence (AI) in risk management, contrasting traditional approaches with those augmented by AI and highlighting the challenges and opportunities that emerge. AI, intense learning methodologies such as convolutional neural networks (CNNs), have been identified as pivotal in extracting meaningful insights from image data, a form of analysis that holds significant potential in identifying and managing risks across various industries. The research methodology involves a strategic selection and processing of images for analysis and introduces three case studies that serve as benchmarks for evaluation. These case studies showcase the application of AI, in place of image processing capabilities, to identify hazards, evaluate risks, and suggest control measures. The comparative evaluation focuses on the accuracy, relevance, and practicality of the AI-generated findings alongside the system’s response time and comprehensive understanding of the context. Results reveal that AI can significantly enhance risk assessment processes, offering rapid and detailed insights. However, the study also recognises the intrinsic limitations of AI in contextual interpretation, advocating for a synergy between technological and domain-specific expertise. The conclusion underscores the transformative potential of AI in risk management, supporting continued research to further integrate AI effectively into risk assessment frameworks.