Predictive policing, a burgeoning application of artificial intelligence (AI) in law enforcement, utilizes algorithms to analyse vast datasets and anticipate criminal activities. This approach aims to enhance resource allocation, improve response times, and ultimately deter crime. However, while predictive policing promises to revolutionize crime prevention, it also raises significant concerns regarding its effectiveness, potential biases, and ethical implications. This study examines how predictive policing algorithm’s function, focusing on their data-driven methodologies and their reliance on historical crime data. Research indicates mixed results regarding effectiveness; while some jurisdictions report reduced crime rates, others highlight issues of accuracy and over-policing in certain communities. Furthermore, these algorithms often reflect societal biases, perpetuating discrimination against marginalized groups and leading to disproportionate surveillance. The ethical implications of deploying AI in law enforcement warrant critical attention, as they intersect with civil liberties, accountability, and public trust. This paper advocates for a balanced approach that incorporates transparency, community engagement, and regulatory oversight in the deployment of predictive policing technologies. Ultimately, the integration of AI in law enforcement must be approached cautiously, ensuring that it serves as a tool for justice rather than an instrument of bias or inequality. By exploring the multifaceted impact of predictive policing algorithms, this study contributes to the ongoing discourse on the future of crime prevention and the responsible use of AI in society.