SummaryThe gamma regression model explores the relationship between a skewed response variable and one or more independent variables. The method of maximum likelihood is popularly adopted to model the relationship. However, the method performance drops when linear dependency exists among the predictors (multicollinearity). In this article, we develop a new method to account for multicollinearity in gamma regression model. The proposed estimator theoretically dominates the existing estimators. The simulation studies and real‐life application supports the theoretical findings.