The application of Artificial Intelligence (AI) techniques to criminal profiling represents a significant innovation in crime analysis and management. This study employs advanced machine learning models, specifically Random Forest and multi-output classifiers such as MultiOutputClassifier, based on the RandomForestClassifier algorithm, to analyze the ISTAT dataset on reported crimes in Italy from 2019 to 2023. The primary objective is to accurately predict both the type of crimes and their geographical distribution. Compared to previous studies in the literature, this approach stands out for its greater comprehensiveness and advancement, not only by utilizing a dataset specific to crimes recorded in Italy but also by integrating multi-output models capable of simultaneously predicting crime types, such as thefts, frauds, and other offenses, alongside their locations. Additionally, the study incorporates advanced preprocessing techniques to enhance data quality and reliability, achieving a high predictive accuracy exceeding 90%. The research concludes with the implementation of interactive geospatial visualizations, facilitating data interpretation and operational application, thereby supporting the planning of crime prevention strategies.