Reinforced Concrete (RC) frame buildings with shear wall are widely used in severe seismic zones. Shear walls are bearing system elements that provide the greatest resistance against horizontal force under the effect of earthquake, limit displacements and prevent torsions. A reinforced concrete shear wall is one of the most critical structural members in buildings, in terms of carrying lateral loads. However, irregular layouts cause to torsional irregularity in buildings. For this purpose, different shear wall frame reinforced concrete building models are designed. The model buildings have a regular formwork plan. The shear wall layout has different variations in each plan. These structure plans were mainly classified in two classes according to their torsional irregularities as structures with torsional irregularities and Structures with non-torsional irregularities. Artificial intelligence (AI) has revolu-tionized industries such as healthcare, agriculture, transportation, and education, as well as a variety of structural engineering problems. Artificial intelligence is transforming decision-making more easier and reshaping building design processes to be smarter and automated. Artificial intelligence technolo-gy of learning from an existing knowledge base is used to automate various civil engineering applica-tions such as compressive strength estimation of concrete, project pre-cost and duration, structural health monitoring, crack detection and more. In this study, it is aimed to determine the structures with torsional irregularity using artificial intelligence methods. Besides, the study is expected to introduce and demonstrate the capability of Artificial intelligence-based frameworks for future relevant studies within structural engineering applications and irregularities.