Geographic Information Systems (GIS) have emerged as critical tools for enhancing public safety through spatial data integration, real-time analytics, and predictive modeling. In rapidly urbanizing environments, the complexity of crime patterns, disaster risks, traffic incidents, and emergency response demands data-driven decision-making frameworks that can capture spatial-temporal dynamics effectively. This study explores the application of GIS in optimizing public safety strategies by integrating multi-source datasets, including demographic information, infrastructure layouts, environmental conditions, and historical incident records. At a broader level, GIS supports urban governance by enabling authorities to visualize risk distribution, identify high-risk zones, and allocate resources efficiently. Through advanced spatial analysis techniques such as hotspot mapping, network analysis, and geostatistical modeling, GIS facilitates proactive risk mitigation and rapid emergency response planning. Narrowing the focus, the study demonstrates how GIS-based risk mapping can improve incident prediction, enhance situational awareness, and support coordinated responses among public safety agencies. The integration of real-time data streams and machine learning algorithms further strengthens predictive capabilities, enabling dynamic adaptation to evolving urban threats. Ultimately, this research highlights the strategic role of GIS in transforming traditional public safety systems into intelligent, resilient, and data-driven frameworks for safer urban environments.