Predictive analytics is reshaping crime mapping and policing by leveraging tools like GIS, machine learning, and big data. This paper reviews current techniques and real-world applications, highlighting their potential to identify crime hotspots and inform proactive strategies. It also examines key challenges, including biased data, ethical concerns, and privacy risks. Through case studies analysis and literature review, the research outlines the limitations of existing systems. The study stresses the importance of fairness, transparency, and accountability. Furthermore, the paper presents a current state of challenges and solutions advocated in research, thus supporting responsible and effective crime prevention practices.