<p>Road Traffic Accidents (RTAs) are a leading cause of fatalities and injuries worldwide, particularly in developing countries where infrastructure and traffic management are often inadequate. This study examines the spatial patterns and determinants of RTAs along the Chiniot-Sargodha Road, Pakistan, by integrating multiple spatial analysis methods to identify accident hotspots and their correlation with demographic, infrastructural, and environmental factors, addressing gaps in the existing literature. The study uses spatial and statistical techniques, including Kernel Density Estimation (KDE), Standard Deviational Ellipse (SDE), and Average Nearest Neighbor (ANN), to analyze RTA clustering patterns from 2012 to 2021. Data from traffic records and geographical databases were integrated with GIS tools to visualize hotspots and assess their association with demographic, infrastructural, and environmental factors. The findings show that RTA hotspots concentrated in central urban areas of Chiniot-Sargodha Road mainly affect younger males (21–30). 2012 stands out due to infrastructure changes, traffic volume, and poor weather. Poor road conditions, inadequate management, and human errors are vital contributors, with a multi-method approach offering a comprehensive understanding of RTA dynamics. This study’s novelty lies in its multi-method approach, combining spatial and statistical analyses to understand RTA dynamics in semi-urban areas comprehensively. The study highlights the need for a multifaceted approach to road safety, combining engineering, traffic management, and education. It provides a framework with practical insights for policymakers and planners, suggesting future research use real-time data and machine learning for improved accident prediction and prevention strategies.</p>