Abstract: This paper introduces a generalized approach structured for the systematic supervising and the performance assessment of drought environments. A significant part of the Time Series Analysis module is taking the history of drought-related parameters measures and giving more detail on the progress and the intensity of the drought esvents. Moreover, Deep Learning algorithms are used to train data sets and to make predictions about future values. The research was done using the multimodal technique (which encompasses machine learning approaches and data-driven analysis based on the satellite images of many sources) for the dispelling of the drought problem in Maharashtra, India in 2018 to 2021 in a bid to disseminate information. With respect to this issue, thorough and prompt drought estimation is a top concern to the successful management of these threats through water resource management. The study adopted the use of new models such as ConvLSTM2D, LSTM models, ARIMA and Random Forest regression to improve drought monitoring. Experimental results were scrutinized and illustrated with performance measures like R-squared, mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). LSTM algorithm demonstrated a very strong potential for learning extended connections and sequences. ARIMA method was more likely to catch the seasonal patterns. The ConvLSTM2D algorithm was enforced to enhance the drought monitoring process. It is as a result of this improvement that both drought evaluation and prediction matured, ultimately enabling more informed decision-making in a bid to manage the crisis as early as possible with mitigation measures being immediately put in place.

Key words: drought monitoring, sensor-based irrigation system, climate change impact, digitalization, integration and hybrid machine learning, remote sensing and climate change impact performance evaluation