Journal of E-Technology Alla A. Kaybiyaynen 17 4 2026 https://doi.org/10.6025/jet/2026/17/4/176-192 https://www.dline.info/jet/fulltext/v17n4/jetv17n4_2.pdf Aviation operations are highly sensitive to rapidly changing meteorological conditions, yet existing research often focuses on isolated airports or specific weather hazards. This study addresses these gaps by developing and analyzing a high resolution, global Meteorological Aerodrome Report (METAR) dataset comprising 958,338 observations from 26 major cities across 15 countries between 2024 and 2026. By integrating meteorological characterization, adverse-weather identification, and machine learning, the study quantifies the temporal and geographical variability of aviation weather conditions. Results reveal a severe class imbalance, with adverse weather events accounting for only 4.29% of observations. While classification models achieved high overall accuracy (up to 98.1%), minority class precision and recall remained low, though fog detection demonstrated strong ranking ability (ROC-AUC = 0.860). Conversely, regression modeling for a continuous severity score proved highly challenging (R² = -1.43), indicating that temperature alone is insufficient for severity prediction. Spatiotemporal analyses highlighted distinct diurnal peaks in adverse weather (11:00–14:00 UTC) and significant geographic heterogeneity. Ultimately, this study provides a comprehensive, multi hazard framework that underscores the need for advanced feature engineering and cost sensitive learning to support risk based operational decision making in global aviation.