This paper focuses on recent trends in mapping unplanned urbanization in informal settlements such as slums, urban villages, shantytowns, and kacchi abadies over the past decade. These areas pose unique challenges for detection due to their diverse characteristics, necessitating advanced mapping approaches. The study reviews literature from 2013 to 2022 sourced from Web of Science and Scopus databases to synthesize scattered data and identify current mapping trends. Four key areas analyzed. First, regional- and country-level mapping trends around the globe, Second, advanced mapping methods and techniques, Third, morphological mapping elements, and last, ontological levels such as, (object, settlement, and environment). The analysis reveals a major focus on mapping unplanned areas is based on the scale of settlements (44%), followed by environment by (32%), and object-level mapping is seen (24%) literature. Remote sensing technologies, particularly Earth Observation (EO) imagery, are widely utilized, with an emerging trend toward integrating these technologies with computer vision algorithms like convolutional neural networks (CNNs), to enhance mapping accuracy. Countries like India, South Africa, and Indonesia article prominently, whereas countries such as Bangladesh, Pakistan, Thailand, Vietnam, Philippines, and Nepal have limited in the literature. The findings underscore the potential application of mapped data in frameworks like IDEAMAPS and ontological frameworks, offering insights beneficial to researchers and data scientists involved in urban development planning. In addition, this review contributes to understanding evolving approaches in mapping informal settlements globally, addressing critical aspects of unplanned urbanization in deprived areas and providing a comprehensive overview of current trends and methodologies.