The growing environmental degradation has exerted desperate burden on resources, therefore, environmental monitoring has become imperative. There is a serious need to • evaluate the quality regularly. Remote Sensing technology has been providing multi- dimensional information, which is utilized in lots of environmental investigations. Geographical Infonnation Systems (GIS) have been accepted as a tumkey solution for the complex world due to its magnanimous breath of functionalities and cost effectiveness. Karachi is one of the worst effected cities of the world due to unchecked and still • uncontrolled air pollution. Spatial variation within metropolis have been largely ignored mainly due to less comprehension, under estimation of spatial techniques as well as difficulties in collecting, processing and analysing the data at micro geographic scales. The main goals of this study are to modulate the infom1ation pertains to air quality and its • adverse effects on human health and find out their spatial pattems all over Karachi. The research has covered different parameters: assessment of land cover / land use classes, . human settlement growth, temporal traffic patterns, population distributions, current level of air pollutants, health implications and public perceptions . . The developed GIS evaluation combined the data sets, various analyses and the resultant maps with the capability to integrate further parameters for future risk assessments. Multi-criteria decision analysis was successfully employed. Micro-geographic appraisals . of the metropolis were perf0l111ed by· considering 58 zones outlined by the local • development authority. Each zonal assessment included area, popUlation density, • distribution of land cover classes,. split of land use categories, frequency of airborne diseases, their prevalence scenario and temporal variations in CO concentrations within the zone. MUltiple regression models for predicting carbon monoxide (CO) enrichment at the olden region of Karachi metropolis have been formulated in which traffic and land use parameters act as independent variables.