Soil drilling operation has become one of the most important interests to researchers due to its many applications in engineering systems. Auger drilling is one of the ideal methods in many applications such as pile foundation engineering, sampling test for geological, and space sciences. However, the dominant factor in determination of drilling parameters drilling operations experience. Therefore, soil-drilling process using auger drilling is studied to obtain the controlling parameters and to optimize these parameters to improve drilling performance which enables proper selection of machine for a required job. One of the main challenges that faces researchers during using of modeling techniques to define the soil drilling problem is the complex nonlinear behavior of the drilled medium itself due to its discontinuity and heterogeneous formation. This article presents two models that can be used to predict the total resistive forces which affect the auger during soil drilling operations. The first proposed model discusses the problem analytically in a way that depends on empirical data that can be collected from previous experience. The second model discusses the problem numerically with less depending on empirical experienced data. The analytical model is developed using matlab® interface, while the numerical model is developed using discrete element method (DEM) using edem software. A simplified auger drilling machine is built in the soil–tool interaction laboratory, Military Technical College to obtain experimental results that can be used to verify the presented models. Data acquisition measuring system is established to obtain experimental results using a labview® software which enables displaying and recording the measured data collected mainly from transducers planted in the test rig. Both analytical and numerical model results are compared to experimental values to aid in developing the presented parametric study that can be used to define the working parameters during drilling operations in different types of soils. Uncertainty calculations have been applied to ensure the reliability of the models. The combined calculated uncertainty leads to the level of confidence of about 95%.