Rapid urbanization is one of the primary reasons for changing the local climate, and there is a high impact on the surrounding areas. Chandigarh is one of the fastest developing cities in India showing rapidly urbanizing agglomeration. Due to the rapid urbanization, natural land surfaces are being replaced by the anthropogenic materials which negatively impacts the ecosystem resulting in urban heat island (UHI) effect. Land surface temperature (LST) is the primary and vital step for the analysis of UHI effect. The present study has been conducted to predict the LSTs for the assessment of UHI effect of the area surrounding Chandigarh city. Remote sensing data from Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) have been used for the prediction of LST. In the study, support vector regression (SVR) model has been developed from LST values of previous three years along with enhanced vegetation index (EVI), road density (RD) and elevation as input parameters to predict LST. The results of the SVR model have been validated using the data of the year 2014. A comparison of the model estimated LST and measured LST indicates that the range of mean absolute error (MAE) and mean absolute percentage error (MAPE) varies between 0.521 K and 0.525 K and 0.181-0.187%, respectively. Hence, SVR model can be used as a significant tool to predict LST for the assessment of heat island effect at any location. From the sensitivity analysis, it is observed that LST was ultimately the most sensitive to the RD compared to EVI and elevation. The SVR model has been compared with artificial neural networks (ANN) model to estimate the skill score factor of the SVR model (Forecasted) with reference to the ANN (Referred) model. Skill scores calculated for the periods show positive values which clearly depicts the efficacy of SVR model compared to ANN model for better LST prediction.