Domestic abuse is a serious social and public health issue that affects millions of people worldwide and can have devastating physical, emotional, and psychological effects. The recent developments in Artificial Intelligence (AI) and Machine Learning (ML) offer flexible solutions to these problems. The use of historical data and decision support systems in AI or multiple linear regression can be useful for the predictive modeling of domestic violence. It also uses NS2 simulation tools to evaluate the performance of prioritization-based communication models specifically for emergency response applications. The authors present empirical results that show substantial improvements to system performance. Random Forest and Logistic Regression algorithms for predictive models found patterns of abuse from unstructured text data with > 90% accuracy. Utilizing SMOTE techniques improved prediction reliability by balancing data. Using NS2 simulation, we observed 20% improved packet delivery ratios and 15% reduced packet loss than the traditional communication models which assure effective emergency communication in emergency conditions. These findings emphasize the potential of AI and ML in combating domestic violence. These technologies provide scalable solutions to policymakers, law enforcement bodies, and social welfare organizations by improving prediction accuracy and communication reliability. This study suggests that the use of AI-based frameworks in public health systems will allow the anticipatory use of interventions, and the efficient allocation of resources will help in decreasing the incidence and impact of domestic violence.