Introduction

The accelerating development of data generation and processing technologies—ranging from environmental sensor networks and the Internet of Things to remote sensing systems and cloud computing infrastructure—has produced an unprecedented volume of environmental data, enabling a more precise understanding of complex climatic processes. Within this context, big data and artificial intelligence (AI) have become fundamental pillars of climate science, not merely as auxiliary computational tools but as infrastructure that has transformed the very nature of research in this field. This transformation is particularly significant given that the Earth, as a complex and dynamic system, is shaped by the continuous interaction of atmospheric, hydrological, biological, and anthropogenic components—interactions that cannot be adequately understood without access to extensive, multi-source, and multi-scale data.

Drought and the intensification of extreme climatic events have emerged as one of the most critical application domains for these technologies. As one of the most consequential environmental, economic, and social challenges of the present era, drought's gradual onset, extensive spatial coverage, and multidimensional consequences make its effective monitoring and prediction dependent on the analysis of vast volumes of heterogeneous data across multiple temporal and spatial scales. Despite growing research on environmental big data, existing studies have predominantly focused either on algorithmic and infrastructural development or on fragmented, discipline-specific applications related to drought, leaving a persistent need for analytical reviews that offer an integrated picture of trends, opportunities, and challenges in this field. The present study aims to provide an integrated analytical framework examining the role of big data and AI in drought monitoring, climate change prediction, and decision support for sustainable natural resource management, with its principal contribution lying in the simultaneous and interconnected examination of big data infrastructure, AI algorithms, and decision-making processes in natural resource governance.

Materials and Methods

This study was conducted using a narrative-analytical review approach. Unlike systematic reviews, which rely on formal screening procedures, strict inclusion/exclusion criteria, and quantitative evidence synthesis, this approach aims to combine, interpret, and critically analyze dispersed findings within the field to construct an integrated conceptual framework of trends, opportunities, and challenges. Given the breadth and interdisciplinary nature of the domain—spanning meteorology, hydrology, data science, and natural resource policy—this approach allows for greater flexibility in thematic analysis and the synthesis of multiple perspectives compared to systematic review methodologies.

Relevant literature was retrieved from Scopus, Web of Science, and Google Scholar using keyword combinations including "Big Data AND Drought," "Machine Learning AND Climate Change," "Remote Sensing AND Drought Monitoring," and "Artificial Intelligence AND Climate Prediction." The analytical process involved three main steps: thematic classification of studies according to their primary application domains (monitoring, prediction, modeling, and decision support); identification of methods, data sources, and technologies employed in each study; and comparative analysis of capabilities, limitations, and research gaps within each domain. In total, 57 sources were directly cited in developing the analytical framework of this study.

Results and Discussion

The review results indicate that the integration of multi-source data—derived from Earth observation systems, ground-based monitoring networks, and big data infrastructure—with AI algorithms enables more precise and real-time drought monitoring, identification of spatiotemporal patterns of climate change, improved prediction of climatic variables, and the development of early warning and decision-support systems. AI methods applied in drought and climate studies were classified into five categories: classical machine learning (e.g., Random Forest, Support Vector Machine), deep learning (e.g., LSTM, CNN), Transformer-based models, Explainable AI (XAI), and physics-informed AI, complemented by hybrid models that integrate physical and data-driven approaches to enhance both accuracy and generalizability.

A comparative examination of widely used climate data repositories—including ERA5, CMIP6, CHIRPS, CRU, MODIS, Sentinel, Landsat, GPM, and GLDAS—revealed substantial variation in spatial and temporal resolution, directly affecting the reliability of AI-based analyses. A comparative table of seven representative applied studies further demonstrated the diversity of algorithms, data types, and evaluation metrics employed across the literature, while highlighting that no single study had comprehensively integrated big data infrastructure, AI algorithms, and decision-making processes—the gap this review specifically addresses.

Despite these advances, persistent challenges were identified across four interconnected levels: data-related challenges (heterogeneity and uncertainty in environmental data), modeling challenges (overfitting and limited interpretability of deep learning models), computational/infrastructural challenges (dependence on costly computational resources, particularly in developing countries), and institutional challenges (the gap between the scientific community and policymakers, and the fragmentation of data across organizations). The analysis further revealed three principal research gaps: limited spatial transferability of AI models across different climatic regions, disproportionate emphasis on meteorological drought relative to hydrological, ecological, and socioeconomic drought types, and the absence of standardized frameworks for evaluating and comparing AI model performance.

Conclusion

The convergence of big data and AI has fundamentally transformed the traditional paradigm of drought and climate change monitoring, prediction, and management, marking a shift from static, retrospective approaches toward dynamic, multi-source, and forward-looking systems. However, fully realizing this potential requires addressing challenges that extend beyond purely technological dimensions to encompass data quality, model interpretability, computational infrastructure, and institutional data governance. Future research directions include the development of hybrid physics-informed AI models, the expansion of explainable AI to enhance trust in decision-critical applications, the advancement of climate digital twins, the integration of big data with IoT and edge computing for real-time monitoring, and the standardization of evaluation frameworks to improve comparability across studies. Ultimately, the effective deployment of big data and AI for sustainable natural resource management and climate resilience depends on aligning technological progress with institutional reform and stronger integration among climate science, data science, and public policy.