Pakistan has experienced worst environmental impacts of heavy rains and flooding during the last decade. These extreme environmental conditions became responsible for the outbreak of many fatal diseases like the sudden outbreak of dengue fever in different cities of Pakistan. The high death toll in Lahore city as a result of dengue fever during the year 2011 became an awakening signal to look into the mysteries and myths behind this disease. The present research intended to study the physical environments that have been responsible to cause the sudden mega outbreak of dengue fever during 2011 in Lahore. The comparison of climatic and social covariates of four selected cities of Pakistan (Islamabad, Rawalpindi, Lahore, and Karachi) has conducted for the years 2009-2012 to analyze the factors that serve and do not serve the spread of dengue fever in urban areas. The reasons and regions of higher risk of dengue fever transmission have been identified by land use classification, processing of digital elevation models, and analyzing the climate and social covariates. Landsat 30 m TM imagery, SPOT 10 m imagery, and SRTM 90 m DEM have been used for the analysis. The Dengue fever case registry, climatic data sets, travelling data, population data, and malaria case registry for the study period have been acquired from respective national departments. The land use 8 classification has done to analyze the change in urbanization over a period of time. DEMs have been processed to identify the drainage patterns and magnitude of drainage density in study areas. The changes in climate covariates like rainfall, temperature, and wind speed; social covariates like population, travelling, change in urbanization, drainage density and patterns have also been analyzed. A macro level study to understand the dengue transmission in urban environmental gradients has conducted comprising the analyses of flow accumulation, drainage pattern, drainage density, change in population, change in urbanization, dengue incidence during 2009-2012, and climate covariates. A micro level study to understand the dengue transmission and identifying the high risk prone localities has conducted comprising the hotspot analysis, outlier analysis, and regression analysis. Furthermore, the relationship of daily dengue fever incidence with climate covariates during the months of July-October for the year 2011 has also analyzed. The aspect of relationship of dengue fever occurrence with other factors and malaria has analyzed to fill the research gap. The relationship between the occurrences of dengue fever and Malaria, dengue fever and flooding, dengue fever and population, and dengue fever and travelling in the study areas for the years 2009-2012 have been taken into account. Linear Regression Model, Generalized Linear Mixed Model (GLM) with Markov Chain Monte Carlo (MCMC) algorithm has computed to see the random effects of different social (population, travelling, and malaria) and climate (minimum- maximum temperature, and rainfall) covariates on dengue fever occurrence. Neural Network with Multilayer Perceptron has used to analyze the normalized importance of different covariates relative to dengue fever occurrence. At the end, the general Dengue prevention and control strategies have been discussed. Results suggest that the low elevation areas with calm winds and higher than the normal minimum temperatures, rapid increase in unplanned urbanization and population, low flow accumulation, and higher drainage density areas favored the dengue fever transmission. The hotspot analysis highlighted the high risk prone urban localities of four cities. Regression model highlighted the risk prone localities and relationship of dengue fever occurrence with population and area of localities. Results show that each dry spell of 2-4 days have provided the suitable conditions for the development and survival of Dengue vector during the wet months of July and August (2011) in the areas of high stream density and population. It has revealed that most of the dengue fever cases reported after the onset of summer monsoon season. Very few cases have been reported in July while higher numbers of cases have reported in the months of August, September, until late October 9 during 2011. Flooding, travelling, population and occurrence of Malaria have significantly affected the occurrence of dengue fever in the study areas. Magnitude of these relationships has also shown by the results of neural network. Change in occurrence of Malaria has affected the occurrence of dengue fever as much as 5.4 times, whereas GLM with MCMC also showed significant random effects of malaria, population and rainfall on the dengue fever occurrence during the studied years (2009-2012). The efficiency of control activities may be improved by highlighting the localities of higher risk within a vulnerable region. Recognizing the high risk areas of dengue fever threat will strengthen the control strategies and support in reducing the impacts for future. Such studies would also be helpful in the decision-making on public health prevention programs. The present study of recent Dengue risk burden and distribution in four major cities of Pakistan will become the basis for future endeavors and help to achieve the goal in mitigation of this dreadly disease.