Using image of satellite and convolutional long short-term memory (ConvLSTM) networks to forecast natural calamities. The increasing intensity and frequency of natural calamities calls for the development of models that can efficiently diminish their impact. This technique aids in predicting natural disasters by combining the meteorological information with satellite images using a ConvLSTM network. This method captures a wide range of filters and resampling methods. The following preprocessing steps are done to improve the quality of the network: normalization and de-noising. The network is designed in a way that it can utilize all satellite image spatial and temporal data. With our exemplary optimization method, the network is able to learn more complex features of the data making damage forecasts more plausible. Our performance framework can forecast hurricanes, earthquakes, wildfires, and floods with accuracy and time in comparison to other methods. Moreover, higher economic efficiency is achieved in regions that experience these events by reducing the reliance on ground-based methods. Furthermore, it is easier for the satellite imagery integrated with ConvLSTM networks to advance early warning systems convoy prompt response strategies to lower the economic effects for all regions prone to natural disasters.