This paper explores the creation of an AI-based system for predicting secure and safe travel routes. Seeing the rise in the amount of criminal activity and traffic accidents, it is now more crucial than ever to have trustworthy and precise tools for determining secure journey routes. To offer users a customized and secure journey path, the suggested system makes use of machine learning algorithms and real- time data from numerous sources, including traffic updates and local crime figures. Such a system in place will ensure the safety and well-being of people walking or driving by alone, especially at night, by preventing them from traveling through an insecure area, despite it being the shorter way out. Besides overviewing the various techniques and strategies employed in models suggested by others in similar style applications, we suggest future paths and our own algorithm for this field’s study and growth, highlighting areas that may be improved and innovative. We have suggested a technique for finding a safe route through a town using hotspot analysis by applying Kernel Density Estimation to find high crime areas and assigning risk rates to different paths based on this data, thus avoiding those routes with high risk as much as possible. This paper’s findings have a significant impact on the way transportation works and can be an effective tool for promoting safety on the roads.