AI Enhance Carpooling App

This paper examines the integration of Artificial Intelligence with the Google Maps API to advance carpooling applications and enhance navigation capabilities. It highlights how AI processes large volumes of geospatial data to enable real-time route optimization, adaptive navigation, and intelligent traffic prediction. The study explores technical methodologies, including machine learning models for forecasting traffic conditions and K-Means clustering for efficient ride grouping and route planning. By augmenting the Google Maps API, AI transforms mapping tools from simple data displays into systems capable of environmental analysis and predictive decision-making. The findings indicate significant benefits across transportation, logistics, and urban planning, where dynamic and efficient navigation is essential. The integration also promotes sustainable mobility through optimized traffic flow and reduced environmental impacts. With strong scalability and global applicability, AI-enhanced mapping systems hold substantial potential for shaping smart cities, improving urban mobility, and supporting connected communities in modern digital ecosystems.