Artificial Intelligence-Driven Crime Prediction Through Historical Data Analysis and Machine Learning Models

Crime prediction has become a critical area of research due to increasing urbanization and the need for proactive law enforcement strategies. This study presents an artificial intelligence-driven framework for predicting crime patterns using historical data and machine learning models. The research integrates rigorous data preprocessing, feature engineering, geospatial-temporal analysis, and supervised learning techniques to develop predictive models capable of identifying crime hotspots and trends. We evaluate Logistic Regression, Support Vector Machines, Random Forests, Gradient Boosting, and Deep Neural Networks using accuracy, precision, recall, F1-score, ROC-AUC, and calibration metrics. Results demonstrate that ensemble methods and gradient boosting achieve superior performance and robustness (Breiman, 2001; Chen & Guestrin, 2016). The system supports decision-making for security agencies while addressing ethical, fairness, and privacy considerations. The study contributes a scalable architecture, reproducible methodology, and practical insights for intelligent crime analytics systems.