Abstract: Rural roads pose a disproportionate risk for roadway fatalities, accounting for 43% of all fatalities despite only 19% of the US population residing in rural areas. This study introduces an integrated framework using advanced computer vision algorithms to enhance rural transportation safety. By harnessing diverse data sources, including video and sensor data, we develop a comprehensive detection and monitoring system. The key functions of the system include vehicle classification and speed estimation in varying lighting conditions (daytime and nighttime), analysis of weather conditions (snowy, rainy, or foggy), assessment of pavement conditions (wet, dry, or flooded), and identification of special objects (such as animals crossing roads). By integrating the system into a broader data infrastructure, our aim is to establish a secure, cost-effective, and scalable foundation to drive innovations and enhancements in rural mobility and safety. This study provides a detailed account of the framework's development and its potential to reduce fatalities and elevate transportation safety in rural areas, aligning with major safety initiatives targeting zero fatalities.