Violence against women is a very complex social problem that essentially requires a good innovative approach to predict it. This study uses of Machine Learning (ML) algorithms to analyze the factors that influence how violence against women, by focusing on identifying influential features that have not been studied in depth by previous researchers. We use the ML algorithms such as CatBoost, LIME, SHAP, and Random Forest as tools to find the main features, and where the dataset used is the secondary data, which of course was cleaned beforehand to eliminate anomalies and data ambiguity. Furthermore, SMOTE was run to balance the classes in the dataset, which was then divided into training and testing sections for model development. The results of this study indicate that the Random Forest algorithm can achieve the best performance metric with 87% accuracy, 80% precision, 94% recall, and 87% of F1-Score. The important features identified from those four algorithms are age, income, and education level, which have a significant influence on the risk of violence. These findings not only provide important insights into the risk factors for violence against women but also emphasize the importance of data-driven methods in prevention efforts and policy formulation. Hopefully, it can be a reference for developing more effective interventions and further research in this area.