Huber loss robust regression and quantile regression are two methods for assessing statistical correlations that provide flexibility and robustness. This study aims to determine the significance of robust and quantile regression models for skewed distributions. The regression models' goodness of fit and accuracy in predicting outputs using simulated data of size one million from skewed distributions with various sets of parameters were analyzed in this study. The two regression models were found to significantly fit skewed distributions across the quantiles.