Subgrade material deformation is one of the most important factors considering for design. For saturated subgrade soils, the compression mainly comes from the extrusion of water from the voids and is shown as the vertical and lateral deformation. Generally, vertical deformation is the main part that must be satisfied within the required limit to make sure the normal use and safety of structures. The subgrade compressibility index is the most important parameter used for calculating subgrade settlement. The main intention that leads this research was the Coefficient of compression (Cc) is a major geotechnical soil parameter required quite frequently for determining settlement of fine-grained soil layers subjected to loads such as that of buildings, vehicles, and infrastructures. If Cc is not known accurately, soil settlement is under- or overestimated, which both cases are not desirable from technical prospective. Thus, determining of Cc is significance importance in settlement calculations. However, measuring Cc using the traditional Oedometer test method is time-consuming, that demands skilled technicians, and requires special laboratory equipment. These bring out that, estimation of Cc from index property data of fine-grained soils are a justified cause. To achieve the goals of the major objective set in this research study, that is to develop a statistical model to predict compression index from index properties of fine grained soils in Azezo Tseda Sub City, Gondar, several steps/phases of studies were made under the different chapters. These studies include: undertaking detail literature review related to this research, conducting index property tests and classification studies, conducting one dimensional consolidation tests and determining compression index parameter; perform multiple regression analysis with subset selection and interaction routines, developing and validation of the newly developed prediction model using primary and secondary data and lastly performing comparative studies of the newly developed prediction model with other similar models. The results from the study suggest, there are different types of soils in the sub city, however, the fine grained soils studied in the Azezo Tseda sub city mainly the Silty Clay and Clay with High and low plasticity is characterized in this study. The main intention was to make the developed models more specific to the corresponding soil types. Based on different index property tests made, the fine grained soils in the region are not expansive since it displays a Liquid Limit (%) range between 45-64% and the %Passing #200 Sieve size are greater than 50%. The Compression Index value calculated from one dimensional consolidation/ Oedometer test outputs determined from e-log p curve for these fine grained soils studied suggest that, the recommended range for settlement computation is between 0.19 - 0.56 with a mean Cc value of 0.37. Here in this study, based on the range of the Cc values, it is concluded that the fine grained soils in the study area are having a medium compressible nature. Moreover, three new simplified statistical models that can predict Compression Index (Cc) value from index property test data more accurately and reasonably are developed, validated and calibrated. These models are one, two and three parameter models respectively for Model 1, 2 and 3, and it is concluded that Compression Index (Cc) value can be predicted more reasonably only from the Atterberg limit test data.The Multiple Regression Analysis showed that there is a relationship between index properties and compression characteristics of the fine grained soils. Especially Plasticity Index for Model 1, Plasticity Index and Liquidity Index for Model 2 and Plasticity Index, Plastic Limit and Liquidity Index for Model 3 are found a significant/powerful variable for prediction of compression index of the fine grained soils found around Azezo-Tseda sub city area. The three Cc predictive models satisfy all the required statistical goodness of fitting measures and the different multiple regression assumptions, however comparison of the new models each other suggest that Model 3 has the highest significance recommended for practice. Comparative studies suggest, the newly developed models are the best fit to the data measured for the study area than when the data fitted with model developed from other regions.