Aerosols in the atmosphere act as a significant meteorological parameter whose presence help us to develop models for long-term prediction of environmental degradation and understand the effects of climate change. Technologies such as remote sensing help us to retrieve aerosol data from space more efficiently and reliably. Although ground level measurement techniques are readily available for determination of aerosol loads over a localized area, aerosol optical depth (AOD) data obtained from satellites provide us reliable information on particulate concentration over a large area. This study portrays the AOD distribution over an urban metropolitan town (Rajarhat) in West Bengal, India, using ESA Sentinel 3 OLCI sensor data. We have used AOD data for four different wavelengths (400nm, 778.75nm, 865nm, and 940nm) and tried to establish the relationship between AOD and land surface temperature (LST) in 4 months (January, April, October, and November) of three consecutive years (2017, 2018, and 2019). Results show that out of the four wavelengths, AOD778.75 nm shows best correlation between aerosol load and LST data in the 4 months of the 3 years. A positive correlation between AOD778.75 nm and LST data obtained for the months of January 2017 (R2 = 0.984), April 2017 (R2 = 0.938), October 2017 (R2 = 0.856), and November 2017 (R2 = 0.695) reflects the rise in aerosol load more in winter (January) than in summer (April) or post-monsoon (October–November) which have a direct effect on the LST. This study also helps to understand the spatiotemporal distribution of AOD over various land use types. Therefore, aerosol data retrieved from Sentinel 3 satellite are useful to understand the temporal variation of LST in a regional scale. This temporal variation will help the planners to take effective decisions to control the rise in LST with increasing urbanization.