Throughout technological developments, women’s safety in today’s society continues to be a critical problem. The disadvantages of current treatments are frequently their mass, discretion lessness, and efficacy. In response, this study presents a brand-new wearable technology that combines regression and kNN algorithms to improve women’s safety. The main goals are to design a small and easy-to-use gadget, utilize the kNN algorithm to forecast the closest safe location, and apply repeated median regression to analyze critical sensor data. This initiative intends to allow women to manage their personal and professional lives with security and confidence by eliminating the shortcomings of earlier systems. The suggested method ensures accessibility and adaptability by allowing for human activation in addition to automating emergency notifications. Furthermore, the system is a viable choice for broad adoption because to its affordability and simplicity of use. To further increase accuracy and efficacy, future improvements may integrate real-time environmental elements like crowd density and time of day. This study represents a major advancement in the use of technology to make spaces safer for women.