Ensuring women’s safety in areas with limited or no internet connectivity remains a critical technological and social challenge. Most existing safety applications rely on continuous network access and single prediction models, which significantly reduce their effectiveness during real-world emergencies. This study introduces AKS-Net (Adaptive KNN–ANN Safety Network), a hybrid machine learning (ML) framework integrated into a smart women’s safety application designed to operate reliably in offline environments. The proposed system combines the locality-based pattern recognition strength of the K-Nearest Neighbor (KNN) algorithm with the adaptive learning capability of Artificial Neural Networks (ANN) to accurately identify potential risk situations. Initially, KNN rapidly detects similar threat patterns using mobile sensor data, including accelerometer readings, motion dynamics, location behaviors, and auditory stress cues. Subsequently, ANN performs refined classification to support robust decision-making. Upon detecting abnormal or dangerous activity, the application automatically activates an offline alert mechanism utilizing Bluetooth, Wi-Fi Direct, and SMS to transmit emergency information to pre-registered contacts and nearby devices. Experimental evaluations show that AKS-Net significantly enhances detection accuracy, minimizes false alarms, and ensures dependable safety alerts without internet connectivity, making it a reliable, adaptive, and energy-efficient solution for next-generation women’s safety systems.