In Kenya, the number of fatalities from road accidents rise year after year due to various causes. However, these numbers differ year after year and it is very difficult to identify the causation making analysis and management of anti-accident public campaigns difficult. With the use of Bayesian networks, the causal analysis can be probabilistically estimated giving a better analysis and therefore better measures in addressing the underlying causes. This paper utilises data from the Kenya National Transport Safety Authority website which is pre-processed and prepared for use in a Bayesian network model. Thereafter a Bayesian network model is built using 70% of the dataset as the training data and 30% as testing data. The model is developed with the aid of the Weka software utilising a sample of 120 instances from the prepared data with 401 instances. Furthermore, to validate the model, a Naïve Bayes model is developed with the same dataset. The Bayesian network model results in 69.125% accuracy which is lower compared to those given by the naïve Bayes model with 72.5% accuracy possibly due to the fact that Naïve Bayes algorithm performs well even with small amounts of data. Also, from the results, the model identifies that most of the accidents are driver related with 63.8% on the Bayesian network and 78.2% on the Naïve Bayes model and therefore more need to be done in addressing the driver causes. However, more variables need to be introduced in the dataset by the transport agency.