Like all infectious diseases, the infection rate of COVID-19 is dependent on many variables. Geographically Weighted Regression (GWR) model and GIS was used to understand the associations between COVID-19 infection rate as a dependent variable and 17 independent variables of air pollution, geo-meteorological and social parameters using a set of temporal data from 2010-2020 (May) in Dhaka, Bangladesh. This study revealed that air pollution parameters like PM2.5 (p<0.02), AOT (p<0.01), CO (p<0.05), water vapor (p<0.01) and O3 (p<0.01) were highly correlated with COVID-19 infection rate while geo-meteorological parameters like DEM (p<0.01), wind pressure (p<0.01), LST (p<0.04), rainfall (p<0.01) and wind speed (p<0.03) were also similarly associated . Social parameters like population density (p<0.01), brickfield density (p<0.02), and poverty level (p<0.01) showed high coefficients as the key independent variables to COVID-19 infection rate. Geographically, significant robust relationships of these factors were found in the middle and southern parts of the city where the reported infection case was also higher. Relevant agencies can utilize these findings to formulate new and smart rules or strategies for reducing infectious diseases like COVID-19 in Dhaka and in similar urban cities around the world.