This study addresses a notable gap in the climate change literature by examining the potential of artificial intelligence and machine learning (AI–ML) in urban climate change adaptation and sustainable development across major global continents. While much attention has been given to mitigation strategies, this study uniquely delves into the AI–ML’s underexplored role in catalyzing climate change adaptation in contemporary and future urban centers. The research thoroughly explores diverse case studies from Africa, Asia, Australasia, Europe, North America, and South America, utilizing a methodological framework involving six-step and five-step models for systematic literature reviews. The findings underscore AI–ML achievements, illuminate challenges, and emphasize the need for context-specific and collaborative approaches. The findings imply that a one-size-fits-all approach is insufficient. Instead, successful adaptation strategies must be intricately linked to the particular characteristics, vulnerabilities, and intricacies of each region. Furthermore, the research underscores the importance of international collaboration, knowledge sharing, and technology transfer to expedite the integration of AI–ML into climate adaptation strategies globally. The study envisions a promising trajectory for AI–ML in the climate adaptation domain, emphasizing the necessity for ongoing research, innovation, and practical AI–ML applications. As climate change remains a defining challenge, this research predicts an increasingly pivotal role for AI–ML in constructing climate-resilient urban centers and promoting sustainable development. Continuous efforts to advance AI–ML technologies, establish robust policy frameworks, and ensure universal access are crucial for harnessing AI–ML’s transformative capabilities to combat climate change consequences.

Deep Learning (DL) is an effective technique for dealing with complex systems. This study proposes a hybrid DL approach, a combination of one-dimensional Convolutional Neural Network (Conv1D) and Multi-Layer Perceptron (MLP) (hereinafter referred to as hybrid Conv1D-MLP model), for multi-stepahead (1-day to 5-day in advance) daily rainfall prediction. Nine meteorological variables, closely associated with daily rainfall variation, are used as inputs to the hybrid model. The causal variables are obtained from a General Circulation Model (GCM). In general, simulation of meteorological variables from GCM is much better than rainfall estimate and observed records of meteorological variables is sparsely available, if not completely unavailable at many locations. Thus, proposed scheme helps to establish the effectiveness of the DL approach in augmenting the quality of rainfall prediction, exploiting the potential of GCM in simulating meteorological variables. The developed hybrid model is applied to twelve different locations in different climatic regimes in terms of daily precipitation characteristics. The proposed hybrid approach is compared with a DL approach namely, Multi-Layered Perceptron (deep MLP) and another machine learning approach namely, Support Vector Regression (SVR). It is also found that the performance of the model gradually decreases as the prediction lead time (in days) increases. Overall, this study establishes the fact that the hybrid Conv1D-MLP model is more effective in capturing the complex relationship between the causal variables and daily variation of rainfall. The benefit is due to the unification of potentials of individual approaches for extracting the hidden features of hydrometeorological association.