Predictive policing, also known as crime forecasting, is a set of high technologies aiding the police in solving past crimes and preventing future ones. With the right deployment of such technologies, law enforcement agencies can combat and control crime more efficiently with time and resources better employed and allocated. The current practices of predictive policing include the integration of various technologies, ranging from predictive crime maps and surveillance cameras to sophisticated computer software and artificial intelligence. Predictive analytics help the police make predictions about where and when future crime is most likely to happen and who will be the perpetrator and who the potential victim. The underpinning logic behind such predictions is the predictability of criminal behavior and crime patterns based on criminological research and theories such as rational choice and deterrence theories, routine activities theory, and broken windows theory. Many jurisdictions in the United States have deployed or have been experimenting with various predictive policing technologies. The most widely adopted applications include CompStat, PredPol, HunchLab, Strategic Subject List (SSL), Beware, Domain Awareness System (DAS), Palantir, and Patternizr. The realization of these predictive policing analytics systems relies heavily on the technological assistance provided by data collection and integration software, facial and vehicle identification and tracking tools, and surveillance technologies that keep tabs on individual activities both in the physical environment and in the digital world. Some examples of these assisting technologies include Automatic License Plate Recognition, Next-Generation Identification System, the Global Positioning System, Automatic Vehicle Location, next-generation police body-worn cameras with facial recognition and tracking functions, aerial cameras and unmanned aircraft systems, DeepFace, Persistent Surveillance Systems, Stingrays/D(i)RT-Box/International Mobile Subscriber Identity Catcher, and SnapTrends (which monitors and analyzes feeds on Twitter, Facebook, Instagram, Picasa, Flickr, and YouTube). This new trend of using predictive analytics in policing has elicited extensive doubt and criticism since its invention. Whereas scholarly evaluation research shows mixed findings about how effectively predictive policing actually works to help reduce crime, other concerns center around legal and civil rights issues (including privacy protection and the legitimacy of mass surveillance), inequality (stratified surveillance), cost-effectiveness of the technologies, militarization of the police and its implications (e.g., worsened relationship and weakened trust between the police and the public), and epistemological challenges to understanding crime. To make the best use of the technologies and avoid their pitfalls at the same time, policymakers need to consider the hotly debated controversies raised in the evolution of predictive policing.