A full-stack web application that provides forecasting of 911 EMS call volumes and locations. The backend is equipped with a PostgreSQL database and machine learning predictive engine. This engine leverages a time-series forecasting model(XGboost and Holt-Winters), specifically trained on historical call data, to identify temporal patterns and predict future EMS call volumes with greater accuracy. The backend then supports users to visualize demand hotspots on the frontend through an interactive heatmap. The application allows users to input a date range to generate and visualize expected call volumes across a City. With expected call volumes, users can make informed decisions on staffing and unit positioning.