The pressing concern of women's safety while transiting in urban settings, particularly within the ride-hailing service-type dimensions, finds an outlet in a search for solutions that ought to be innovative and resilient. The project proposes an integrative platform providing three critical features, that is, AI-assisted real-time monitoring of ride-sharing services, crowdsourced safe route maps, and a voice-activated incident logging tool. Powered by highly sophisticated machine learning algorithms, the system is capable of monitoring driver behavior, identifying risks and other unsafe situations by analyzing telematics data such as speed, and route deviations, among others. The platform is designed so that users can share their feeling of safety concerning the roads in the city, thus allowing a dynamic representation of safer routes to take. A hands-free command feature permits women to report incidences of harassment or unsafe situations in real time, and such reporting is logged and analyzed almost instantly. The platform improves itself with each threat reported and was built based on feedback from users and incidents reported, making it a very proactive data-driven solution meant to enhance women's safety during commutes. Hence, through a pilot phase of the project, the intention lies in allowing development of a system based on its contextual use in the real world as well as to expand its reach, hence providing safer urban transit for women across the world.