###### Abstract  
The integration of Artificial Intelligence (AI) and smart technologies into safety management is a pivotal aspect of the Fourth Industrial Revolution or Industry 4.0. This study conducts a systematic literature review to identify and analyze how AI and smart technologies enhance safety management across various sectors within the Safety 4.0 paradigm. Focusing on peer-reviewed journal articles that explicitly mention \"Smart\", \"AI\", or \"Artificial Intelligence\" in their titles, the research examines key safety management factors, such as accident prevention, risk management, real-time monitoring, and ethical implementation, across sectors, including construction, industrial safety, disaster and public safety, transport and logistics, energy and power, health, smart home and living, and other diverse industries. AI-driven solutions, such as predictive analytics, machine learning algorithms, IoT sensor integration, and digital twin models, are shown to proactively identify and mitigate potential hazards, optimize energy consumption, and enhance operational efficiency. For instance, in the energy and power sector, intelligent gas meters and automated fire suppression systems manage gas-related risks effectively, while in the health sector, AI-powered health monitoring devices and mental health support applications improve patient and worker safety. The analysis reveals a significant trend towards shifting from reactive to proactive safety management, facilitated by the convergence of AI with IoT and Big Data analytics. Additionally, ethical considerations and data privacy emerge as critical challenges in the adoption of AI technologies. The study highlights the transformative role of AI in enhancing safety protocols, reducing accident rates, and improving overall safety outcomes across industries. It underscores the need for standardized protocols, robust AI governance frameworks, and interdisciplinary research to address existing challenges and maximize the benefits of AI in safety management. Future research directions include developing explainable AI models, enhancing human-AI collaboration, and fostering global standardization to ensure the responsible and effective implementation of AI-driven safety solutions. artificial intelligence; safety management; Safety 4.0; Industry 4.0; smart technologies; proactive safety +
Footnote †: journal: Applied sciences  
Industry 4.0 maximizes the efficiency and flexibility of production systems, enabling customized mass production and enhancing competitiveness through the efficient use of resources and optimization of production processes [1; 2]. However, these technological innovations drastically change existing industrial structures and work environments, potentially causing new types of risks and safety issues [3; 4]. For example, the adoption of CPS and IoT increases cybersecurity risks, and unexpected accidents may occur in the interactions between automated systems and human workers [2; 3]. In this context, Safety 4.0 has emerged as a new safety management paradigm within the Industry 4.0 environment [5; 6]. Safety 4.0 aims to prevent accidents and enhance safety awareness by leveraging advanced technologies and integrating the core technologies of Industry 4.0 into safety management to simultaneously improve safety and productivity [3; 6]. [PERSON] and [PERSON] [1] analyzed the impact of Industry 4.0's core technologies on manufacturing systems and suggested that these technologies could also be applied to safety management. AI has emerged as a key element in implementing Safety 4.0, enabling prediction and real-time response through its ability to analyze vast amounts of data and recognize patterns [2; 7]. For instance, machine learning algorithms can detect anomalies in equipment in advance to prevent breakdowns, and deep learning-based image analysis technologies can monitor hazardous factors in the work environment in real time [2; 8]. [PERSON] et al.