Corrosion of reinforced concrete beams (RCBs) poses a critical issue in structural engineering, leading to reduced shear capacity and compromised structural integrity. Traditional methods for predicting the shear capacity of corroded reinforced concrete beams (CRCBs) are often complex, time-consuming, and struggle with nonlinear behaviors. Thus this research introduces the “Explainable BeamNet-12” model, a deep learning-based approach for accurately predicting the shear capacity of CRC beams, with a focus on improving corrosion prevention strategies. Explainable BeamNet-12 is built on an artificial neural network (ANN) framework and incorporates explainability features using SHAP, making it both accurate and transparent. The model is trained on a diverse dataset parameters that includes corrosion levels, reinforcement properties, and beam geometry, ensuring that predictions are contextually relevant and precise. Explainable BeamNet-12 outperforms other state-of-the-art models, such as Extreme Gradient Boosting (XGBoost), AdaBoost, and ANN, achieving an R-squared value of 0.998, an RMSE of 4.649, and an MAE of 3.087. Its explainability features allow structural engineers to gain valuable insights into the factors influencing shear capacity, enabling better decision-making for maintenance and reinforcement.