Urban safety agencies increasingly require data-driven systems capable of anticipating and localizing criminal activitybefore it occurs. Crime Analytics Pro presents an integrated framework that combines machine-learning-based prediction, time-seriesforecasting, and geospatial hotspot visualization to support proactive decision-making. The platform automates preprocessingofmulti-source crime datasets, extracts temporal and spatial features, and employs ensemble and regression models suchas RandomForest, Linear Regression, and clustering techniques (K-Means, DBSCAN) to model crime patterns across regions and timehorizons.Forecast outcomes are visualized through Folium-based interactive maps and an intuitive Streamlit dashboard designedfornontechnical users. The system emphasizes interpretability through feature-importance evaluation, correlation analysis, andforecastconfidence intervals while maintaining modular scalability. By merging predictive analytics with geospatial intelligence inaunifiedenvironment, Crime Analytics Pro enables a comprehensive, transparent, and replicable approach to crime preventionandsmart-citygovernance. Keywords: Crime Prediction; Machine Learning; Random Forest; Time Series Forecasting; Hotspot Mapping; GeospatialIntelligence; Folium; Streamlit Dashboard; Public Safety Analytics; K-Means; DBSCAN; Urban Crime Forecasting; Interpretability;Cross-Validation