This article aims to present a structured literature review that utilizes computational intelligence techniques, specifically fuzzy logic, neural networks, genetic algorithms, and machine learning, to assist in the assessment of workplace risk from human factors. The general aim is to highlight the existing literature on the subject, while the specific goal of the research is to attempt to answer research questions that emerge after the review and classification of the literature, which are aspects that have not previously been addressed. The methodology for retrieving relevant articles involved a keyword search in the Scopus database. The results from the search were filtered based on the selected criteria. The research spans a 40-year period, from 1984 to 2024. After filtering, 296 articles relevant to the topic were identified. Statistical analysis highlights fuzzy systems as the technique with the highest representation (163 articles), followed by neural networks (81 articles), with machine learning and genetic algorithms ranking next (25 and 20 articles, respectively). The main conclusions indicate that the primary sectors utilizing these techniques are industry, transportation, construction, and cross-sectoral models and techniques that are applicable to multiple occupational fields. An additional finding is the reasoning behind researchers’ preference for fuzzy systems over neural networks, primarily due to the availability or lack of accident databases. The review also highlighted gaps in the literature requiring further research. The assessment of occupational risk continues to present numerous challenges, and the future trend suggests that fuzzy systems and machine learning may be prominent.

This paper investigates the effectiveness and limitations of the traditional Fine-Kinney method for occupational risk assessment, emphasizing its shortcomings in addressing complex and dynamic work environments. To overcome these challenges, two advanced methodologies, Fine-Kinney10 (FK10) and Fuzzy Fine-Kinney10 (FFK10), are introduced. The FK10 employs a symmetric scaling system (1–10) for probability, frequency, and severity indicators, providing a more balanced quantification of risks. Meanwhile, FFK10 incorporates fuzzy logic to handle uncertainty and subjectivity in risk assessment, significantly enhancing the sensitivity and accuracy of risk evaluation. These methodologies were applied to a linemen workshop in an energy production and distribution company, analyzing various types of accidents such as falls from heights, exposure to electric currents, slips on surfaces, and more. The applications highlighted the practical benefits of these methods in effectively assessing and mitigating risks. A significant finding includes the identification of risks related to falls from heights of <2.5 m (SH1) and road traffic accidents (SH6), where all three methods yielded different verbal outcomes. Compared to the traditional Fine-Kinney method, FK10 and FFK10 demonstrated superior ability in distinguishing risk levels and guiding targeted safety measures. The study underscores that FK10 and FFK10 represent significant advancements in occupational risk management, offering robust frameworks adaptable across various industries.