A new framework named HYSTL has been introduced to improve crime prediction in urban environments by addressing the challenge of varying crime data across different cities. This knowledge-guided hypernetwork approach aims to create a more unified and effective crime predictor, enhancing public safety. By leveraging this methodology, HYSTL seeks to overcome inconsistencies in crime data that typically hinder the development of universal predictive models. The framework’s design focuses on learning from diverse urban crime patterns to provide a generalized solution applicable across multiple locations. Initial assessments suggest that HYSTL is a promising advancement in the field of crime prediction, offering potential improvements over existing models. This development aligns with ongoing efforts to integrate artificial intelligence techniques into public safety applications, as reflected in related recent studies. Overall, HYSTL represents a significant step toward more reliable and scalable crime forecasting tools.