Crime is a persistent social issue that impacts public safety and urban development. With the rise in data availability and machine learning techniques, predictive modeling of crime rates has become a valuable tool for law enforcement and policy planning. We suggest a combination machine learning strategy in this paper that integrates both spatial and temporal data, alongside social and economic indicators such as decographic and Economic Indicators rate, literacy, and income levels, to enhance crime rate prediction accuracy. We evaluate the effectivness of multiple models, including RF, XGBoost, and a hybrid ensemble of both, on a real-world dataset comprising crime statistics from multiple Indian states. Our results demonstrate that integrating socio-economic factors significantly improves model performance, offering deeper insight into crime patterns and enabling data- driven intervention strategies. The proposed model outperforms traditional single-model baselines, achieving higher accuracy and F1 scores across various crime categories. This approach serves as a robust framework for smart policing and proactive crime prevention in high-risk zones.