Accurate crime prediction is crucial for effective law enforcement and security, enabling proactive resource allocation and risk reduction. Criminal behavior is influenced by complex, diverse socio-economic factors, necessitating advanced models capable of extracting intricate patterns from large datasets. This research presents a methodological and applied comparison of four primary categories of time series forecasting models: Statistical Models (AutoARIMA), Machine Learning models (AutoLightGBM), Deep Learning models (N-HiTS), and Foundation Models (TimeGPT). The study’s innovation lies in (1) integrating these diverse categories in a single comparative framework tailored for security decision-makers, (2) explicitly applying cutting-edge AI, particularly Foundation Models (TimeGPT) with pre-training on vast, multi-domain time series, for crime prediction for the first time, and (3) demonstrating a comprehensive application using daily crime data from Chicago (2017–2019), with the final month serving as a challenging test set for assessing robustness against sudden fluctuations. Results indicate that Foundation (TimeGPT) and Deep Learning (N-HiTS) models outperform in accuracy, effectively capturing nonlinear relationships and complex seasonality. Statistical (ARIMA) and traditional ML (LightGBM) models offer greater interpretability and faster training but are less adept at handling unexpected surges. This comparative, automated approach offers a practical solution for security agencies seeking AI adoption without significant programming complexity. The research underscores time series modeling’s role in enhancing security operations and explores new avenues for AI-driven proactive crime prevention using big data.