The application and efficiency of artificial intelligence-based algorithms in improving women's
security systems is investigated in this research article. Different machine learning models and
their uses in real-time threat detection, emergency response optimization, and predictive
analysis of possibly hazardous environments are investigated in this work. This study assesses
the effect of AI-driven security solutions on women's safety by means of a mixed-method
approach combining quantitative analysis of system performance measures with qualitative
evaluation of user experiences. Comparatively to conventional security measures, the results
show a notable increase in threat detection accuracy and reaction times. The paper also tackles
the ethical issues and privacy problems related to AI-based monitoring systems and offers
suggestions for creating more strong and user-centric security solutions.