Recommending safe routes has become a fundamental necessity due to the increasing number of accidents. This heavily relies on how to evaluate the road severity. Existing recommendation systems are based on user feedback, either positive or negative. However, this type of evaluation overlooks many aspects of road severity like accident history and volunteered geographic information on road conditions. To fill this gap, we elaborate a comprehensive and predictive road risk analysis, relying on objective and subjective data. To recommend safe roads, we propose an algorithm called Safe-Path based on accurate and reliable risk values. To validate our approach, we conduct some experiments to benchmark various machine and deep learning models.