Drought is a natural event that slowly deteriorates water reserves. This study aims to develop a machine learning–based computational framework for monitoring drought status in water‐scarce regions. The proposed framework integrates the precipitation index (PI) with support vector machine models to forecast drought occurrences based on an autoregressive modelling scheme. Due to the suitability of the PI for drought analysis in arid climates, the developed hybrid model is appropriate in regions with limited rainfall. This study used a historical precipitation dataset from 1958 to 2020 at the Kuwait International Airport, Kuwait City. The study area is characterised by scarce rainfall and is vulnerable to severe water shortages owing to limited water resources. Initially, historical PI time‐series datasets were examined for stationarity to validate the utility of the autoregressive model. The autocorrelation function test was significantly associated with the PI time series at the 12‐ and 24‐month drought‐monitoring scales. Predictive drought forecasting models were constructed to predict drought occurrences up to 3 months in advance. Statistical evaluation metrics were used to assess model performance for the 12‐ and 24‐month drought‐monitoring scales. The results showed a strong association between the observed and predicted drought events, with coefficients of determination (R2) ranging between 0.865 and 0.925 for the 12‐ and 24‐month drought‐monitoring scales. The proposed computational framework aims to provide water managers in arid and water‐scarce regions with efficient and reliable drought‐monitoring tools to assist in preparing appropriate water management plans. This study provides guidance for improving water resource resilience under water shortage scenarios in the study area and other climatic regions by applying suitable drought indices in conjunction with robust data‐driven models. The results provide a baseline for water resource policymakers worldwide to establish sustainable water conservation strategies and provide crucial insights for drought disaster preparation.
