Mixture distribution modeling has a long history in statistical literature and exploding applications in various fields. It is used to model population heterogeneity and for providing a general framework for clustering and classification. Most studied and applied mixture models in statistics are Gaussian mixture models. In this work, we study a univariate uniform-Laplace mixture distribution. We develop its basic theoretical properties and show its equivalence to a symmetrized uniform-exponential mixture distribution. We also investigate parameter estimation connected with the model using maximum likelihood estimation approach.