The primary purpose of geospatial intelligence is to avail a maximum number of people to hospitals by actively locating nearby hospitals based on real-time location data. This research presents an AI-based hospital tracking development system through K-means clustering and GPS-driven Geohashing for more precise and timely hospital recommendations. The proposed system clusters hospitals using K-means, thus decreasing search complexity and increasing retrieval time. Moreover, spatial indexing with GPS-driven Geohashing enables the quick and accurate identification of hospitals. On top of these, predictive analytics are enhanced by machine learning for analyzing patient distribution trends and predicting high-demand areas for proper allocation of resources. The system was extensively tested with geospatial datasets, which is an indication of its viability in practice. This application proposes a very scalable and efficient solution for healthcare navigation ensuring timely accessibility to medical facilities, while at the same time enhancing the user experience. This proposed method also achieved accuracy of 84% which marked a significant change than the other tracking systems.