This study analyzes the relationship between air pollution (PM2.5 levels) and temperature across major cities of Pakistan using R Studio. A simulated dataset was created for five cities — Karachi, Lahore, Islamabad, Peshawar, and Quetta — to demonstrate the process of environmental data analysis. Statistical summaries, correlation, and graphical visualization techniques were applied to explore the variation of PM2.5 concentrations with temperature. The results indicate that temperature has a moderate correlation with PM2.5 values, suggesting potential climatic influence on air quality. Visualization through scatter plots, regression lines, and boxplots enhances understanding of pollution trends. This approach demonstrates how R Studio can be effectively used as a tool for environmental monitoring, data visualization, and statistical interpretation in developing countries.