Dynamic environmental circumstances can sometimes be incompatible with proactive human intentions of being safe, leading individuals to take unintended risks. Behaviour predictions, as performed in previous studies, are found to involve environmental circumstances as predictors, which might thereby result in biased safety conclusions about individuals’ inner intentions to engage in unsafe behaviours. This research calls attention to relatively less-understood worker intentions and provides a machine learning (ML) approach to help understand workers’ intentions to engage in unsafe behaviours based on the workers’ inner drives, i.e., personality. Personality is consistent across circumstances and allows insight into one’s intentions. To mathematically develop the approach, data on personality and behavioural intentions was collected from 268 workers. Five ML architectures—backpropagation neural network (BP-NN), decision tree, support vector machine, k-nearest neighbours, and multivariate linear regression—were used to capture the predictive relationship. The results showed that BP-NN outperformed other algorithms, yielding minimal prediction loss, and was determined to be the best approach. The approach can generate quantifiable predictions to understand the extent of workers’ inner intentions to engage in unsafe behaviours. Such knowledge is useful for understanding undesirable aspects in different workers in order to recommend suitable preventive strategies for workers with different needs.