Skewed models are important for analyzing data, and the mixture inverse Gaussian (MIG) distribution is a flexible model with good properties. This paper discusses different aspects of the MIG distribution for modeling positive data, including transformations, moments, and fitting models. Real examples from engineering, environment, insurance, and toxicology show that the MIG distribution fits the data well. The researchers also introduce new parametric distributions through transformations of the MIG model.