The Soil Water Retention Curve (SWRC) is an essential input for modeling contaminant transport, irrigation scheduling, and other vadose zone soil-water processes. Measurement of SWRC requires instrumentation, skill, time, and cost. Alternatively, SWRC can be estimated from basic soil properties such as bulk density, particle size distribution (PSD), void ratio, plasticity index, etc., but the accuracy of estimates for varying soil textures remains largely unexplored. In this study, the performance of SWRC estimation models based on soil texture was analyzed for improved efficiency and application. Four samples (sand, silt, loam and clay) from the Unsaturated Soil Hydraulic Database (UNSODA) and two field soil samples (silt loam and sandy loam) from agricultural soils of Lower Himalaya were used in the study. Ten SWRC models were categorized and compared based on their applicability and efficiency. Overall, Fredlund and Xing (F&X) based models showed better performance in estimating SWRC for selected soil texture classes. Correlation analysis revealed a significant influence of soil texture class on model performance. Coefficient of variation (CV) showed that the wet region of SWRC was well captured by all models compared to the dry region. Based on performance, SWRC models were ranked for respective soil texture classes and further evaluated for dry to saturated moisture conditions. Recommended models for respective soil texture classes can be used to estimate SWRC as per the availability of input soil properties, eliminating the need for complex experiments, thus saving considerable time and effort with acceptable accuracy.