Growing intricacy of crime calls for sophisticated law enforcement methods. To improve police efficiency, this paper suggests an artificial intelligence-powered Smart Surveillance and Law Enforcement Optimization System combining machine learning, predictive analytics, and automated crime reporting. For real-time decision-making and criminal pattern identification the system uses Random Forest and K-Means clustering. Empirical analysis shows a notable 30% decrease in reaction times and a corresponding increase in crime prediction accuracy (by 85%). Automated record keeping also improves data access and reduces human mistakes. User comments show that law enforcement personnel find efficiency and simplicity of use to be quite satisfactory. The results confirm how well artificial intelligence-driven law enforcement systems address community involvement, resource allocation, and crime prevention. Future improvements will center on using social media analytics and improving forecasting models for certain crime trends. This study emphasizes how transforming artificial intelligence may be in contemporary police.