Background Effective safety training in high‐risk industries including construction, mining, manufacturing, aviation and nuclear energy is critically important, as inadequate hazard awareness and human error consistently contribute to occupational fatalities and severe incidents. Method This systematic review synthesises evidence from 66 studies published between 2015 and 2026 across 29 countries to evaluate the application and effectiveness of virtual reality (VR) and augmented reality (AR) technologies for safety training in these sectors. A comprehensive literature search was conducted across Scopus and Web of Science. Results and Conclusions The findings show that VR demonstrates consistent effectiveness for deep skill acquisition and hazard recognition through high‐fidelity simulation of environments too dangerous for real‐world practice, evidenced by its universal adoption in mining and predominant use in nuclear emergency training. AR provides complementary value for situated learning and just‐in‐time performance support, enhancing procedural accuracy and situational awareness in construction, manufacturing and railway contexts. Extended reality (XR) configurations facilitate collaborative training for dynamic risk scenarios requiring integration of real and simulated elements. Sector‐specific adoption patterns reflect strategic alignment between technological affordances and industrial priorities, with aviation exhibiting the most sophisticated behavioural assessment frameworks. However, critical gaps constrain the evidence base: longitudinal assessments examining knowledge retention beyond immediate post‐training remain exceptionally rare; the predominance of student samples limits ecological validity; assessment beyond Kirkpatrick Level 2 (learning) is exceptional; and the absence of standardised competency metrics hinders cross‐study synthesis. This review concludes that immersive technologies represent promising complementary tools for safety training, offering distinctive pedagogical affordances that address limitations of conventional methods.
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