Road transport is fundamental in encouraging economic growth, creating employments, and providing individuals with access to essential services. However, accidents on roads represent one of the primary causes of death and injury globally, with a significant impact on economic and health sectors, there by deteriorating the quality of life in a society. This paper uses Machine Learning strategies to analyze road accident behavior in Latin American countries, in contrast with Australia and the United Kingdom. Results suggest that the causes and characteristics of accidents in different regions vary according to the type, age and sex of those involved, as well as temporal aspects that suggest moments of greater risk considering the day, month and years.