Regions located in semi-arid climates are highly affected by changes in environmental parameters, such as vegetation, rainfall, groundwater, soil moisture and temperature. A multi-view drought analysis considering multiple types of drought (hydrological, meteorological, vegetational and relative). This analysis is needed to deal with such a complex interaction of many factors. Multi-view drought analysis derived from Landsat 8/9 data was applied to categorize the Tamilnadu districts, South India, into five hierarchical severity levels: no, mild, moderate, severe and extreme drought. Further, four machine learning models (linear regression, RF, SVM and XGBoost) were applied to examine the error metrics from the long-term meteorological drought, with XGBoost and Linear regression, giving generally good performances with R(2 )around 0.9. The high to moderate risk of water demand mainly occurs in the middle and southern portions of the study area, with less risk of water demand in the northern portion. The spatial distribution of drought indicators across the Tamilnadu districts provides insights for water engineers, NGOs and public authorities to prioritize the planning of suitable water resources, eventually increasing drought resilience to climate change at the district level.