As most disasters are weather-related, location tracking is crucial during any event, and early warning systems can save lives. This article outlines the design and execution of a wireless emergency alerts model for catastrophic alerting based on data from distributed databases by using machine learning enabled Application Programming Interface (API) services. Using machine learning-based historical and current data from the NASA Open API and OpenWeatherMap API, this program is intended to forecast natural catastrophes and aid in their management. To forecast future disasters, the application analyses data from previous natural disasters, including floods, wildfires, tornadoes, cyclones, and hurricanes. Additionally, it gives users access to GPS (Global Positioning System) based evacuation routes and emergency shelter information. The purpose of this article is to assist individuals in the lead-up, during, and aftermath of a disaster by providing access to important resources and information. The software serves as a daily catastrophe management tool, notifying users of the most recent disasters and letting them know whether an incident is happening right now using machine learning-based data. This application can be an important tool to help individuals from disaster.