A new Gumbel kernel estimator was developed to estimate probability density functions accurately, especially for data with limited support. This estimator outperformed the traditional Weibull kernel estimator in terms of bias, variance, and optimal bandwidth. The Gumbel kernel estimator is non-negative, asymmetric, and achieves the best rate of convergence for error minimization. This method is useful for accurately estimating probability density functions in various fields.