In today’s unpredictable world, detecting a safe road route for journeys on the fly is a big challenge. The crime rate in various areas which a route encompasses can vary a lot. Hence, the presented paper focuses on detecting safe road routes by examining crime rates across various districts along all the route alternatives, and the objective is to suggest the safest path to a given destination. Additionally, the paper has proposed a system that provides details about the safety rating of the route, total travel time, minimum toll tax, and other features of the route showcasing the results on a map so that users can make an informed choice about their commute. The paper utilizes data from the National Crime Records Bureau of India to establish a reliable crime database. It utilizes a machine learning model to predict average crime rate along the route and evaluate the likelihood of encountering unsafe situations along the route. Further, the proposed work aims to integrate real-time safety assessments for the user’s current location to provide necessary information to individuals for making informed commuting choices to create a comparatively secure landscape.