Navigation apps usually minimize travel time or distance, which can send drivers through accident-prone or congested stretches. This paper presents NAVISCAPE, a navigation platform that ranks candidate routes using accident-severity prediction, traffic forecasting, live traffic data, and nearby emergency resources. An eXtreme Gradient Boosting (XGBoost) classifier was trained on 95,723 road-accident records from Karnataka, a Long Short-Term Memory (LSTM) network forecasts hourly vehicle counts, and live segment speeds come from the TomTom Flow Segment API. Routes generated through OpenStreetMap/OSRM are scored on safety (40%), traffic (30%), estimated travel time (20%), and distance (10%).

The platform also indexes 2,226 hospitals and 921 police stations, accepts crowdsourced hazard reports, and prepares authenticated SOS alerts for up to four trusted contacts. On a stratified test set of 18,790 records, XGBoost reached 52.47% accuracy, with a recall of 0.88 for grievous-injury cases. In one route comparison, nearby historical accidents fell from 37 to 8 and estimated travel time from 42.0 to 33.5 minutes. The results are encouraging, but they rest on a regional dataset and a single case study, so broader validation is still needed.