The seismic response of reinforced concrete (RC) shear walls is of paramount importance in earthquake-resistant design. While traditional techniques such as finite-element-based modeling and experimental testing provide comprehensive insights into shear wall behavior, they often require significant computational resources and time. In response, this study investigates the efficacy of various Deep Neural Networks (DNNs), including Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and LSTM-Based Autoencoders, in predicting the behavior of RC shear walls under monotonic and cyclic loading conditions. Through training on a diverse dataset encompassing various geometric configurations, material properties, and loading scenarios, the proposed approach generalizes well to unseen cases, enabling rapid estimation of shear wall responses without extensive numerical simulations. This approach offers several advantages, including faster design iterations, improved assessment of existing structures, and potentially higher accuracy. Extensive comparative analyses against experimental tests and numerical simulations evaluate the performance of the deep learning models. Results demonstrate that the proposed approach achieves superior accuracy in prediction of hysteresis and pushover curves, effectively capturing the nonlinear structural behavior of RC shear walls subjected to static and cyclic loading conditions. Validation using a comprehensive experimental database of 160 specimens shows that the model achieves less than 10% error in maximum lateral load predictions for specimens within the training data range; while simultaneously reducing computational time Overall, this study contributes to the evolving landscape of structural engineering by showcasing the potential of DNNs as efficient tools for predicting the behavior of RC shear walls.