Devastating natural disasters called cloudbursts typically happen in Himalayan regions during the rainy season. As an example, consider the recent floods in the Uttarakhand region of “Kedarnath,” which were triggered by a cloudburst that ravaged the nation and killed thousands of people in addition to animals and flash floods in the Mandakini River. Cloudburst prediction is typically achieved using weather forecasting, data mining techniques for weather prediction by modeling meteorological data, and laser beam atmospheric extinction measurements from human and unmanned aerospace vehicles. This research proposes an Arduino-based cloudburst predetermination system that calculates rainfall intensity in real time. Forecasting cloudburst phenomena has proven to be an enormous challenge for many meteorologists and rain experts. The forecast of cloudburst is made more difficult by the very nature of cloudburst occurrence. Because cloudburst downpours are confined to a very small geographic area and happen over a short period of time, they are highly challenging for meteorologists to forecast. The authors of this paper offer a cloudburst prediction model that predicts when a cloudburst will happen in a certain location using deep learning techniques. The authors developed the model following their collection of data regarding cloudburst episodes that had occurred in Uttarakhand, India, during the preceding 10 years. In the experiments, time series sequence models, namely Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM), were employed. The Predictive Power Score (PPS) has been utilized to extract the salient features that are fed into these sequence models. When compared to other sequence models, the GRU-based model has demonstrated potential based on an analysis of the models’ accuracy and loss function.