Death, injury, and disability resulting from road traffic accidents remain significant global public health issues. Both developed and developing nations face challenges stemming from growing human and vehicle populations. Consequently, mitigating the severity of traffic accidents is a critical focus for traffic agencies and administrations. This study utilizes a missing value imputation algorithm and Naïve Bayes conditional independence to predict potential injuries following a traffic accident at a specific location in real-time. Using historical traffic accident data from Australia, the research aims to estimate the likely severity of traffic accidents after they occur. The goal is to assist decision-makers in allocating appropriate resources to aid injured individuals at the accident scenes.