Bangladesh faces recurring air-quality alerts, yet division-level evidence that unites real-time pollutant data with routinely observed weather conditions remains scarce. We compiled 100 consecutive days of hourly measurements for PM2.5, PM10, NO2, O3, CO, SO2, and Air Quality Index (AQI) across all eight divisions, integrating matched meteorological variables (temperature, humidity, wind profiles, pressure, precipitation, and cloud cover). Using foundational statistical techniques descriptive summaries, frequency distributions, and visual analytics we identify spatial contrasts and diurnal cycles, documenting, for example, that the most industrialized divisions experience evening PM2.5 surges and a higher share of “Unhealthy” AQI hours. Correlation exploration further highlights how calm winds and elevated pressure sustain pollutant loads despite rainfall events. The study supplies the first beginner-built, division-spanning statistical baseline on Bangladesh’s air pollution dynamics, offering ready-to-use evidence for municipal planners while laying out a transparent framework future student teams can extend with causal or predictive models.