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The grain-size distribution (GSD) curve is done by using Sieve analysis and Hydrometer analysis, then it is used to determine basic soil properties such as optimum moisture content (OMC), and maximum dry density (MDD), the suggested model is dependent on the retaining percent of soil from the (GSD) curve as independent variables. In this research, appropriate steps techniques were employed to find the OMC and MDD of the soil. The data analysis is done by the SPSS program by minimizing the sums of difference squares and increasing the square of Pearson product correlation coefficient R2 of given data to predict the above aim. The analysis strategy is done by Six-stages, which depend on:-the retaining percent of soil at different points on the GSD curve, increasing the points on the GSD curve in each step, and replacement between clay size fractions<0.002mm with clay size fractions< 0.005mm. Test results shows that useful functional relationships exist between the compaction parameters from Particle-Size Analysis routine testing of soil. It is concluded that the Square of Pearson product correlation coefficient R2 for correlation between the experimental and predicted values increases when increasing the number of grain sizes in the model. It also concluded that the OMC, and MDD can be predicting efficiently from grain size analysis only without depending on any other physical or chemical property of soil. [ABSTRACT FROM AUTHOR]