Laboratory experiment is the main tool for determining fluid properties such as saturation pressure, formation volume factor, fluid density and viscosity. However, both sampling and experiments are limited by cost and time. On the other hand, the information about the fluid properties are essential for reservoir engineering works such as reserves and productivity calculation. Therefore, empirical correlations are commonly used as an alternative method. Researchers developed empirical correlations by curve-fitting experimental data. The variation in the correlations is partly due to different datasets. It explains why each correlation more accurately estimates the fluid properties in some cases/regions than in the other cases/regions. In this study, we propose a technique of using surrogate models and the available laboratory database to estimate the fluid properties. Two surrogate models are studied in this paper e.g. universal kriging and neural networks. A comparative analysis is being performed between the proposed technique and known correlations used in the industry. The study shows that the proposed method demonstrates better estimation than the published empirical correlations. This paper has a potential to become a guideline for engineers who would develop an estimation tool with the use of experimental fluid database.