The deficiency in precipitation over the longest period has a significant effect on the increased wildfire risk, agriculture, and water resource planning, as droughts can have severe economic, social, and ecological impacts that hinder the realization of the United Nation’s sustainable development goals including climate action, Life on Land, and life below Water. Traditional drought monitoring methods repeatedly rely on hydrological, meteorological, and agricultural data, which can limit the availability of accurate and timely information. The increased utilization of expert systems such as artificial intelligence entailing machine learning and deep learning leveraging large datasets and advanced algorithms offer the potential to improve drought prediction, detection, and characterization, have demonstrated efficient approaches to drought monitoring where these technologies are implemented providing more efficient, accurate, and scalable solutions for drought monitoring. The world experienced three longest drought episodes that occurred between July 1928 and May 1942 (the 1930s Dust Bowl drought), July 1949 and September 1957 (the 1950s drought), and June 1998 and December 2014 (the early 21st-century drought), which were not monitored. This study reviews available deep and machine learning techniques that have been introduced to predict and monitor drought. The reviewed literature demonstrates that DL has generalization, scalability, handling sequential, missing, structured, and unstructured, large and complex data, automatic feature learning, handling non-linear relationships, improved performance, and predictive modeling abilities. Besides these model merits, existing challenges such as model interpretability, data quality, and computational requirements need to be addressed to realize their full potential. With continued development and research, machine and deep learning are poised to play an increasingly important role in mitigating the impacts of drought and enhancing resilience to climate variability.