With the increase in construction activity, the lack of availability of natural aggregates has become a major concern in the world. As the connectivity of road transport is of much importance, its construction also needed to be appropriate. So due to this, research has been done to use alternate materials in any layer of the pavement. One of the best-recycled materials is reclaimed asphalt pavement (RAP) material and its consumption in the top layer has been studied by many researchers. Now, this RAP could be used in base, and sub-base layers also. Thus, with varying percentages of RAP, the cement-treated base properties are defined. With the advancement of technology, the world is moving toward the use of artificial intelligence (AI). So, in this study, an appropriate ANN model is developed to determine the unconfined compressive strength of cement-treated base samples. Two models with varying input values are developed. An effective model is determined among these by comparing their performance properties.