Due to climate change worldwide, the frequency and severity of extreme weather events such as drought are suddenly increasing, which has devastating effects on the fragile environment and human society. In the present investigation, to assess meteorological and agricultural drought conditions in Maharashtra state, India, will be done using various meteorological drought indices like standard precipitation index (SPI), evapotranspiration index (SPEI), rainfall anomaly index (RAI), and agricultural drought assessment will be done using satellite-based drought indices such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Vegetation Condition Index (VCI). The severity, frequency, and intensity of meteorological drought conditions will be assessed for the period 1901 to 2020. For this research, we will use non-parametric statistical techniques like the Mann-Kendall test and Sen’s method for innovative trend analysis to detect long-term trends, the Pettit test, Standard Normal Homogeneity Test (SNHT), and the Buishand Range test to detect change points. We will apply Artificial Neural Networks for the future forecasting of drought conditions.