Hit-and-run accidents are a significant concern that not only damages property but also endangers human lives. The objective of this research was to develop an SQL-based predictive model capable of accurately and efficiently predicting the risk of hit-and-run incidents. In this study, data collected from the Montgomery County, Maryland data.gov website, covering the period from January 2015 to May 2025, was utilized. The study employed a hybrid SQL architecture, including SQLite and DuckDB, for data preparation, real-time processing, machine learning integration, and geospatial analysis. The SQLite database is used for data storage and initial comprehensive analysis using Magic SQL to enable faster and more organized data evaluation. DuckDB, a high-performance database engine, powered scikit-learn for in-memory machine learning predictions, offering speed advantages over traditional methods utilizing pandas. The Random Forest model identified high-risk zones for hit-and-run crashes in Montgomery County, Maryland, through an interactive heatmap, serving as a powerful tool for real-time risk analysis. This study contrasts the performance of a traditional machine-learning model with a DuckDB-powered model, revealing that the DuckDB-enhanced approach, utilizing larger datasets, results in expedited and optimized analysis. This case study offers an impactful solution for a risk zone identifier and law enforcement for hit-and-run crashes.