Predictive analytics, powered by data, statistical algorithms, and machine learning, is revolutionizing decision-making across industries like business, finance, healthcare, and law enforcement. This paper examines its applications in decision-making processes, predictive policing, and evidence analysis, emphasizing both benefits and ethical considerations. Predictive analytics aids organizations in making data-driven decisions, optimizing operations, and managing risks. However, ethical concerns regarding data privacy, algorithmic bias, and accountability require careful attention. Predictive policing utilizes historical crime data to prevent criminal activities, but concerns arise about potential biases and discrimination in AI systems, necessitating measures for fairness and transparency. AI-driven evidence analysis, including DNA analysis and gunshot detection, enhances forensic science and criminal investigation. Yet, ethical considerations surrounding data privacy and misuse must be addressed. In integrating AI, stakeholders must prioritize ethics, fairness, and justice, utilizing AI as a supportive tool for human decision-making. Addressing these concerns unlocks AI's potential while safeguarding rights and values.