The measurement of soil temperature profiles for different locations or climates is essential for calculating the thermal performance of applications connected with the soil, e.g., underground heat storage systems. Estimating soil temperature profiles is identified as crucial knowledge for plant and crop growth as well as for germination in all agricultural tasks. The ground temperature depends on weather conditions (ambient temperature, solar irradiation, wind velocity, sky radiation, etc.) that contribute to the resulting temperature distribution within the soil close to the surface. In literature, several approaches have been discussed to predict soil temperature in different climates and locations, such as data-driven models, wavelet transform artificial neural networks, statistical models, etc. However, these models require extensive data sets from literature and high computational efforts. In the present study, a one-dimensional analytical model will be presented, which is based on the Green’s Function (GF) method. The model can estimate the daily and annual variation of the soil temperature distribution at different depths from real-time weather data sets. The model was experimentally validated with an accuracy of more than 96%. The significant advantage of the presented analytical method is the low computational cost, which is lower than that of numerical models by approximately two orders of magnitude.