Deltas are highly sensitive to microclimatic variations resulting from both natural and anthropogenic influences. The Danube Delta, one of Europe’s most ecologically significant wetland systems, is increasingly affected by atmospheric changes that threaten its biodiversity, water resources, and agricultural productivity. Monitoring microclimate variability in such regions is essential for developing informed adaptation strategies. This chapter addresses the need for precise forecasting tools by integrating traditional and AI-driven models for climate trend analysis. This chapter directly links advanced modelling objectives with the microclimate challenges of the Danube Delta. The primary objective of this study is to enhance understanding of microclimatic variability in the Danube Delta by leveraging both traditional statistical and advanced deep learning techniques. Specifically, the study analyzes long-term climate data from 1990 to 2025 to monitor key atmospheric parameters and identify seasonal patterns and trend-related changes. It compares the forecasting capabilities of Seasonal AutoRegressive Integrated Moving Average (SARIMA) with Gated Recurrent Unit (GRU) and Transformer models and explores the potential of hybrid modelling approaches to improve predictive accuracy. This research contributes to the development of scalable, and data-driven frameworks for climate monitoring and the formulation of targeted adaptation strategies in delta regions. This study utilized hourly climate data from the Copernicus ERA5-Land dataset, focusing on three key variables: 2-meter temperature, surface pressure, and wind speed. The data were aggregated into monthly means to emphasize long-term trends and reduce short-term fluctuations. A two-tiered modelling framework was applied, combining traditional SARIMA with GRU and Transformer deep learning models. The GRU model demonstrated the highest accuracy for temperature forecasting, while the Transformer model outperformed others for surface pressure predictions. Interestingly, SARIMA yielded the best results for wind speed, although none of the models achieved high predictive accuracy for this variable. These findings highlight that no single model universally excels across all parameters, reinforcing the need for context-specific model selection in microclimate forecasting. This chapter demonstrates that the integration of statistical and AI-based models can significantly enhance forecasting capabilities for microclimate monitoring in complex ecosystems, such as the Danube Delta. Deep learning models show strong potential for capturing nonlinear patterns in temperature and pressure, while traditional models may still hold value for variables with less predictable behavior. The proposed framework offers a scalable and transferable approach to support climate adaptation planning, biodiversity protection, and sustainable development in vulnerable deltaic environments, advancing the goals of the Deltas of the World series to promote innovative, data-driven approaches to delta sustainability.