Abstarct: Regression analysis is, in fact, the most widely used method amongst statistical techniques and a statistical tool for identifying the relationship between one or more covariates with a response variable. When a linear regression model is not efficient for analyzing a data set, we can use non-parametric regression methods. Splines as one of the interpolation tools are a powerful nonparametric method for modeling nonparametric regression. Splines construct curves that include polynomials of the same degree on substructures of a specified interval and join together with defined contiguous conditions, and pass through common knots between the two infrastructures. During the years since the introduction of Splines, their theoretical foundations have been developed, and different versions have been introduced. Penalized Splines (P-Splines) are among the most widely used tools for nonparametric modeling and smoothing problems. The appearance of P-Splines with many changes in smoothing problems has become a running and active field of research. Due to the broad applications and importance of Splines, we are going to introduce them and their growth as well as the development process and some of their features.