<p>Particulate matter (PM) is considered one of the principal air pollutants that degrade air quality and increase human health risks. Landscape pattern impacts PM_2.5, and understanding PM_2.5 in response to the landscape pattern is crucial for sustainable urban health. In this study, the influence of landscape configuration and composition PM_2.5 using landscape metrics (e.g., percentage of landscape (PLAND), number of patches (NP), patch density (PD), largest patch index (LPI), Shannon’s diversity index (SHDI), contagion (CONTAG), Shannon’s evenness index (SHEI), aggregation index (AI) or total area (TA)) land use data integrating remote sensing in Kolkata megacity region (KMR), Eastern India. The impact of PM_2.5 on human health has been assessed based on the questionnaire survey. A correlation and regression analysis were also carried out to assess the relation between PM_2.5 and landscape fragmentations and to study the effect of the landscape pattern on PM_2.5. A principal component analysis was also carried out to understand the association between PM_2.5 and landscape patterns in winter and summer. The results showed that: (i) the PM_2.5 in winter was higher than in the summer; (ii) the industrial and educational zones had a higher PM_2.5 concentration than the cultural and residential zones (iii) built-up area had a significant impact on PM_2.5 concentration in winter; (iv) PD, ED and AI had significant positive correlations with PM_2.5 concentration, while SHEI had a significant negative correlation; (v) regression results showed that Land use type (winter and summer) and Landscape pattern (summer), had a higher capacity to predict PM_2.5. Vegetation predicted better PM_2.5 concentration than built-up in land use type (summer) and Landscape pattern (winter and summer). The findings of this study suggest that land use planning and policies are essential to reducing PM_2.5 concentration in cities.</p>