Natural disasters are very tragic and hard to understand, so we need quick and effective ways to respond. Machine learning methods are completely new when it comes to improving real-time disaster reactions. Their research is very important for allocating resources, making predictions, and coming up with a plan for how to respond. This study gives an in-depth look at the current state of machine learning methods used in crisis reactions and gives useful suggestions for making them better. This study investigates SVM, RNN, and Random Forest, which are three well-known machine learning methods. Through the combination of different methods, our suggested combined technique can improve crisis forecast, resource distribution, and reaction planning. One important thing that this study adds is a new way of doing things that takes advantage of the best parts of each program while also being able to change to real-time data. This method helps to make crisis reactions more accurate and on time. After a full review, we show that our strategy works by comparing it to the results of well-known, traditional approaches. Time to react, accuracy, precision, recall, and efficiency of resource allocation are all metrics where the suggested strategy routinely surpasses conventional approaches. Storms, fires, earthquakes, floods, tsunamis, and avalanches are just some of the hazard scenarios that our method has been validated for using real-world data and simulations. Finally, our study paves the way for future work that might include machine learning methods in catastrophe response plans implemented in real time. Our technique improves response effectiveness, optimizes resource allocation, and generates accurate and timely forecasts by combining Random Forest, RNN, and SVM. These findings show that machine learning has great promise for better disaster management, which in turn might lessen the toll that natural catastrophes have on people and their possessions.