Conventionally, the deep beam shear strength is analyzed with codes (mechanics and empirical models). The purpose of this investigation is to provide an alternative way of accurately estimating the shear capacity of Reinforced Concrete Deep Beams (RCDBs), including those with and without shear reinforcements (WOR and WWR), by adopting machine learning models. Four machine learning algorithms: k-Nearest Neighbor (kNN), Random Forest, M5Rules, and Sequential Minimal Optimization for Regression (SMOReg), were considered, and the selection was based on their performance in previous related studies. A database of 733 samples for WWR and 378 samples for WOR was compiled, utilizing 14 and 8 input features, respectively, in each case. WEKA, an open-source software suite, was used in preprocessing the data and also tuning the hyperparameters. SMOReg beat other models for WOR with an R² value of 0.9607, while Random Forest did best for WWR with an R² value of 0.9667 in the testing sets. The shear strengths predicted by the machine learning models were compared to four traditional standard codes. The results show that the machine learning models beat conventional methods by a large margin, while also being consistent with earlier models generated using machine learning. This demonstrates the model's prediction accuracy and robustness.