<p>Urban air pollution, strongly influenced by meteorological variability, poses major challenges for rapidly expanding coastal megacities. This study develops a transparent, stepwise framework to diagnose urban atmospheric behavior, applied to hourly data from six monitoring stations in Istanbul (2019–2024). The framework integrates differential and integral distributions with trend analysis and temporal correlation to uncover seasonal and multiannual patterns in key meteorological parameters (precipitation, humidity, pressure, temperature, wind speed, direction) and pollutants (PM₁₀, SO₂, CO, NO_x), supported by geospatial diagnostics of elevation, slope, aspect, land use, and vegetation cover. Results reveal significant warming trends in summer and winter, while precipitation remained stable. Wind speed emerged as the dominant dispersive factor, with temperature and surface pressure modulating pollutant accumulation. Extreme pollution events exhibited distinct diurnal and seasonal signatures: PM₁₀ exceedances in Esenyurt and CO peaks in Aksaray occurred mainly at night and in winter, whereas SO₂ anomalies in Alibeyköy reflected localized combustion. In contrast, coastal and vegetated stations such as Beşiktaş and Avcılar recorded lower exceedance frequencies, highlighting the buffering role of maritime ventilation and ecological cover. Geospatial synthesis confirmed that low-lying basins and incremental urban expansion (~ 18% vegetation decline) amplify pollution risks, while stable vegetation and coastal slopes mitigate them. This dual-layered framework enhances interpretive clarity and reproducibility across diverse urban contexts. Findings are relevant for coastal megacities, where meteorological–pollution–geospatial linkages can guide station placement, emission zoning, vegetation preservation, and seasonal mitigation. Ultimately, the study underscores the importance of integrated diagnostics to support climate-resilient, sustainable urban planning.</p>