The relationship between greenhouse gases (GHGs) and temperature increase is fundamental to understand climate change. Therefore, this chapter delves into the intricate relationship using time series by least square method and multiple linear regression analysis. Global temperature and GHGs data observed at Global Monitoring Observatory has been used. Findings revealed that the concentrations of CO2, CH4, NOx and SF6 have consistently been rising during the observed time and global mean surface temperature is found to be highly correlated with GHGs concentration. Regression showed a positive trend in all four variables and statistically positive association was found between CO2 versus temperature, CH4 versus temperature and a positive correlation between NOx versus temperature. The value of multiple correlation coefficient (R) is 0.828, indicating a good prediction level, while the coefficient of determination (R2) is 0.685 which implies that explanatory variables, i.e., CO2, CH4 and NO, explained 68% of the variability in temperature (F (3, 13) = 9.444, p < 0.005). CO2 is found to be statistically significant (p < 0.05) to the prediction. This study emphasizes that there is a critical need to reduce greenhouse gas emissions to prevent looming climate calamities by highlighting the worrying pattern of rising global surface temperatures, which is mostly attributable to emissions caused by human activity.