This study presents the development of an interactive analytical framework that leverages forensic data from the Minas Gerais Civil Police’s internal system (PCNET). The dataset, comprising all forensic attendances from January 2019 to December 2024, was initially subjected to data cleaning and normalization using Python—ensuring the removal of personal information and irrelevant fields. The processed data was then imported into Power BI, where interactive dashboards were built to facilitate comprehensive statistical analyses and dynamic visualizations of forensic operations across the state. Key findings include the identification of forensic units with higher demands for specific types of examinations, insights into the annual trends of forensic activities, comparative analyses among units aimed at balancing the number of criminal experts, and evaluations of external attendances that involve field operations. Furthermore, state-wide analyses of selected crime categories, such as homicide, violent crimes, and digital forensics, revealed notable variations in case volumes over time. The proposed framework enhances decision-making processes in forensic service management by providing timely and actionable insights, thereby demonstrating the potential of integrated data analytics in optimizing public safety operations.