Estimating surface-level fine particulate matter from satellite remote sensing data can expand the spatial coverage of ground-based monitors. This approach is particularly effective in assessing rapidly changing air pollution events such as wildland fires that often start far away from centralized ground monitors. We developed Deep Neural Network algorithm to bias correct hourly PM2.5 levels informed by GOES-R satellites, NOAA meteorology forecasts, and real-time PM2.5 observations from the Environmental Protection Agency (EPA) via AirNow. The surface-satellite-model collocated datasets for the period of 2020-2021 was used to assess the biases in GOES-GWR PM2.5 against AirNow measurements at hourly and daily scales. Then a deep neural network (DNN) based bias correction algorithm is used to improve the accuracies of GOES-GWR PM2.5. The DNN uses GOES-GWR PM2.5, GOES-R aerosol parameters, and HRRR meteorological fields as input and AirNow PM2.5 is used as target variable. The application of DNN reduced the PM2.5 biases as compared to GOES-GWR estimates. RMSE was also reduced to 6.55 µg/m3 from 8.72 µg/m3 in GOES-GWR estimates. The DNN model was also evaluated on two sets of independent datasets for its robustness. In the first independent dataset for the first half of 2020, ~89% of stations show an increase in correlation (r) and, ~76% and ~62% of stations show a reduction in bias. The IOA and r for the independent data were 0.77 and 0.61 (GWR: 0.68 and 0.53) and RMSE was 4.48 µg/m3 (GWR=6.13 µg/m3) for the same period.