Occupational incidents in construction workplaces present a persistent challenge, with severe consequences for both workers and project outcomes. This study aims to examine the specific causal factors contributing to these incidents, focusing on the interaction between worker-related, environmental, and procedural factors. The objective is to identify key contributors to workplace accidents and develop a predictive model to mitigate future risks. (Mahamulkar, Lad and Patel 5–7 September 2022). To achieve this, we employed a machine learning approach on a comprehensive accidentology dataset collected from contractors across multiple construction sites. The dataset includes variables such as worker experience, environmental conditions, adherence to safety protocols, and more. By analyzing direct, indirect, and root causes, the methodology uncovers hidden patterns and interdependencies that traditional analysis might overlook (Kim and & Chi 2021). The study’s findings indicate that worker experience and environmental factors are the most significant contributors to incident occurrence, with a clear interaction effect between these variables (Shuang and & Zhang 2023). The results not only confirm previous research but also offer enhanced predictive capabilities for future safety measure. (Zhu, Du and & Tang 2020). This research demonstrates the value of machine learning in generating data-driven insights, ultimately aiding in the development of targeted interventions to improve safety standards in the construction industry.