Droughts are one of the most harmful natural hazards, which affects the availability of water, agricultural productivity, ecosystems, and the livelihood of human beings. Climatic changes have increased the severity as well as occurrence of drought events on a large scale across the globe, thus increasing the urgency to develop effective mitigation and adaptation strategies. Conventional drought prediction and management techniques are usually limited by slow responses, coarse spatial scales and absence of integration of climatic and socio-economic information. Artificial Intelligence (AI) introduces new solutions that can help alleviate these limitations by processing large and complex data to detect trends, make predictions, and aid decisions. The current review explores the growing role of AI in enhancing drought resilience, especially the implementation of the technology in early warning systems, real-time monitoring, efficient water resource management, agricultural adaptation, and policymaking. We examine the key AI techniques, which include Machine Learning (ML), Deep Learning (DL), and Evolutionary Algorithms, including Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO), and their applicability in the drought research. This paper also focuses on current limitations of AI and the future potential for integrating AI technologies into sustainable drought risk management systems.