This study investigates the integration of crowd-sourcing, mobile applications, artificial intelligence (AI), and geospatial analysis to address wildlife-vehicle collisions. The research demonstrates how crowd-sourcing, facilitated through user-friendly Android and iOS applications, can effectively gather extensive and timely data on such collisions. The collected data is processed using ResNet-50, achieving a classification accuracy of 91% for identifying animal species involved in accidents. The study further employs geospatial tools to visualize accident hotspots and analyze the environmental conditions surrounding these incidents. By leveraging GPS data and Weather API integration, the research provides a comprehensive understanding of the factors contributing to wildlife-vehicle collisions. The data is stored and managed using MongoDB, which supports seamless integration with AI models and web applications, allowing for real-time data visualization and analysis. The findings suggest that these technological advancements can significantly enhance efforts to mitigate wildlife-vehicle collisions and inform policy and infrastructure improvements. Future work could involve expanding data collection to broader regions, enhancing AI models, and incorporating predictive analytics for proactive prevention strategies.