Road traffic crashes remain a major public health concern in low- and middle-income countries, where limited surveillance infrastructure, fragmented institutional data systems, and manual reporting processes contribute to significant under-reporting and delayed response. Rwanda reflects this challenge, with non-fatal crashes frequently undocumented and critical information often unavailable to emergency responders, insurers, and policymakers. This study proposes and implements a citizen-centered crash reporting system designed to improve real-time crash documentation in resource-constrained environments. The prototype combines a smartphone application and USSD interface to enable inclusive participation across diverse mobile device users. The mobile application supports multi-modal reporting including text, images, video, and audio and automatically captures geolocation and weather context, while simulated integration with vehicle and insurance databases demonstrates potential for institutional interoperability. The USSD channel enables rapid reporting in low-connectivity contexts, capturing core incident details when smartphone access is limited. Together, these components illustrate a scalable model for enhancing crash visibility, enabling timely response, and strengthening evidence-based road safety planning. Future development will focus on connecting with live government and insurance systems, extending multimedia analytics through machine learning, and incorporating complementary open-source data streams to further enrich crash intelligence.