Knowing hydrocarbon dew point conditions in natural gas (HCDP) are becoming widely important in the modern marketing of liquid and gas. Without determination dew point conditions (Pressure & Temperature), the accurate description of phase changes and phase behavior cannot be achieved. Numerous models for only predicting dew point pressure of gas condensate have been proposed and other for wet natural gas reservoirs, but there is no model for predicting both dew point pressure(DPp) and dew point temperature (DPT) for wet natural gas reservoirs. Some of the published models assume knowledge the reservoir fluid composition (requiring laboratory experiments to be performed), while others only require field parameters such as reservoir temperature, stock-tank oil API, and the condensate-gas ratio (CGR). The primary objectives of this paper are to determine dew point temperature (DPT) and dew point pressure (DPp) correlations using a fluid database of nearly fifty-six wet natural gas reservoirs, that is very important for engineers to understand and manag wet natural gas reservoirs. This model was made using multiple least-square nonlinear regression analysis methods to find hydrocarbon dew point conditions (HCDP) as a function of [CGR, Tr, API gravity, γw, γC7+, MWw and C1, C2, CO2, N2 mole %] with a different constant value. Both statistical and graphical accuracy ensures that new models are more accurate in predicting hydrocarbon dew point conditions (HCDP) in comparison with equations of state by using limited data. Finally, thirty-four new separate set of measured data were used in testing models with the excellent agreement as compared with laboratory works.