Artificial Intelligence in Urban Climate Prediction and Sustainability: A Systematic Review of Methods, Data, and Challenges.

Abstract

Urban climate variability has become a critical concern due to rapid urbanization and its impact on environmental sustainability. Artificial Intelligence (AI) has emerged as a powerful tool for modeling and predicting complex urban climate patterns by leveraging large-scale heterogeneous data. This paper presents a systematic review of recent advancements in AI-driven urban climate analytics, focusing on machine learning and deep learning techniques applied to temperature prediction, air quality assessment, and microclimate modeling.

A structured review methodology was adopted to analyze studies published between 2018 and 2025 from major scientific databases, including IEEE, Springer, Elsevier, and Scopus. The selected studies are categorized based on modeling approaches, data sources, and application domains. The review highlights the increasing adoption of convolutional neural networks (CNNs) and hybrid models for capturing spatial–temporal dependencies.

Despite notable progress, challenges such as limited data integration, poor model generalization, and lack of interpretability remain. This paper identifies research gaps and proposes future directions including multi-modal data fusion and explainable AI for sustainable urban climate management.