The study's goal is to find out how actionable knowledge affects police performance and crime prevention, as well as how data-driven insights might strengthen policing tactics and raise public safety. Principal subjects: Data-driven policing: The function of intelligence-led policing, predictive policing, and data analytics in contemporary law enforcement. The definition, traits, and advantages of actionable intelligence are discussed. Police accountability and Performance: How actionable intelligence affects police performance indicators including clearance rates, arrest rates, and reaction times. Partnerships and community engagement: The importance of partnerships and community involvement in preventing crime, as well as the utilization of actionable intelligence to foster trust. Bias and ethical Issues: The potential for bias in the application of algorithms and prediction models, as well as the ethical ramifications of employing data-driven police. Important Points/Results: Data-Driven policing: Targeted policing tactics, crime trends, and future crime predictions can all be informed by effective data analysis. Actionable Intelligence: Police decision-making, resource allocation, and operational effectiveness can all be enhanced by timely and pertinent intelligence. Police Performance and Accountability: By raising arrest rates, lowering crime rates, and raising public satisfaction, actionable intelligence can improve police performance. Community engagement: Police can promote cooperation and establish confidence by incorporating the community in the creation and application of data-driven policing tactics. Ethical considerations: When implementing data-driven policing, it is imperative to address ethical issues like privacy, bias, and openness. Approach/Methodology: qualitative research. Comprehensive interviews: To collect rich qualitative data, conduct semi-structured interviews with community people, crime analysts, and police personnel. Focus Groups: To learn more about the opinions and experiences of police officers and community members, arrange focus groups. Document analysis: To find trends and patterns, examine police records, crime data, and other pertinent documents. Analyze Case Studies: To investigate the application and results of data-driven policing tactics, conduct case studies of certain police departments. Implications and Significance: Enhanced Public Safety: Data-driven policing can help create safer communities by empowering law enforcement to proactively handle criminal activity and disturbances. Improved police performance: police accountability, efficacy, and efficiency can all be increased with actionable intelligence. Informed Policymaking: The results of this study can help shape evidence-based policing procedures and policies. Ethical considerations: The study will emphasize how crucial ethical issues are when applying data-driven policing. Law enforcement organizations are the audience. Academics, community organizations, policymakers, and the general public. In conclusion, the goal of this study is to add to the expanding corpus of research on data-driven policing and how it affects police performance and crime prevention. Law enforcement organizations, legislators, and scholars will gain important insights from the study's examination of the theoretical underpinnings, empirical data, and practical applications of this strategy. The ultimate objective is to use data and intelligence strategically to increase public safety and policing effectiveness