A great deal of attention has been applied recently to machine learning (ML) algorithms to solve difficult engineering problems in the field of structural engineering. Using borrowed features of ML algorithms (implemented), a solution to one of the most troublesome problems in concrete structures—namely, shear—is proposed. The understanding of shear failure in reinforced concrete (RC) structures has led to numerous laboratory investigations and analytical studies over the last century. Due to decades of efforts afforded by researchers, significant experimental shear test results have been created and archived. This data provides an opportune environment to implement ML techniques and evaluate model efficiency and accuracy. The focus of this paper is on ML modeling of the shear-transfer mechanism for slender RC beams without transverse reinforcement. Test results for 1149 RC beams were incorporated in the ML analysis for training (80%) and testing (20%) purposes. Prior to the ML analysis, a correlation coefficient analysis was conducted to determine if given design parameters affected shear strength. When compared to the data used, code-based shear equations provided with large safety margins gave reasonable predictions. Exponential-based Gaussian process regression (GPR) ML models yielded comparable predictions. Of the 19 ML models employed, most were considered as an effective strength predictive tools. These ML model predictions were compared to each other and with design provision shear equations.