Density is one of the most important fuel properties influencing the injection, spray, and combustion characteristics. Since a number of articles have recently been performed investigating the effects of biodiesel-diesel-alcohol ternary blends on combustion characteristics and exhaust emissions, the reliable density data and regression models for the ternary blends become more important in developing accurate spray, combustion and emission models. However, there is lack of studies which focus on (a) the measurement of densities of ternary blends including higher alcohols over wide alcohol blending ratio at different temperatures, (b) development of one-dimensional regression models to predict densities of ternary blends, and (c) comparison of predictive capabilities of models with artificial neural networks (ANNs). Therefore, in this article, to eliminate the lack of such studies in the existing literature, waste cooking oil biodiesel was produced, and it was mixed with diesel fuel and several alcohols to prepare ternary blends. The density measurements of ternary blends were performed under various temperatures (278.15 K-368.15 K). The exponential equation which can be used in spray or combustion model was derived by fitting the density data. The predictive capability of the exponential model was compared with the linear model and ANN using different density data of ternary blends to determine the best-fit correlation. According to results, ANN is the most suitable one to estimate density; however, the exponential model is also thought to be an alternative to ANN.