ABSTRACT: A simple linear regression is commonly used as a practical predictive pricing model of used cars. The concept of maximum predictive pricing or efficient frontier model is a popular notion in capital asset pricing model. A non-linear model has been observed to provide a better estimate of price appreciation while describing real-life phenomena. The objective of this paper is to develop a new S-curve model on the maximum predictive pricing of used cars. A dynamic S-shaped Membership Function (SMF) was used as a base function to formulate a new S-curve maximum equation model. A comparison between linear regression, cubic regression, and S-curve model has been made prior to formulating a new S-curve maximum predictive pricing model. Validation of the new S-curve maximum predictive pricing model was done by curve-fitting the new equation into the required data set. This new S-curve model is expected to represent a better and practical model related to the price prediction of used cars and specifically, can offer a more realistic forecast of used car pricing in Malaysia.