Agriculture is significantly impacted by drought because of the lack of forage and water, which reduces crop yields and impacts livestock. Due to the spatiotemporal variability, it is challenging to predict the occurrence and extent of the drought. Accurate forecasting of drought conditions is crucial for the sustainable cultivation of Brassica crops, as it empowers farmers to make well-informed decisions regarding water management and variety selection to mitigate the negative impacts of drought. Utilizing historical data and analyzing intricate datasets to uncover complex patterns, machine learning (ML) algorithms have proven to be a powerful tool in predicting droughts with high accuracy. ML algorithms have been used by various researchers in order to predict drought conditions at particular places. This is further helpful in making proactive measures for drought mitigation and resource management. This chapter performs an extensive review of popular machine learning algorithms for the effective prediction of drought situations.