Cement-admixed clay (CAC) is a widely-used soil stabilization technique for enhancing the strength and stiffness of soft clay. However, the stress-strain behavior of CAC is complex and nonlinear, and also depends on various factors such as mixing proportion, confining pressure, stress path, and shearing condition. In this study, we propose a novel approach for modeling the stress-strain behavior of CAC using recurrent neural networks (RNNs), which are a type of deep learning (DL) technique that can well capture the temporal dependencies and nonlinearities in sequential data. We compare three types of RNNs: traditional RNN, long short-term memory (LSTM) neural network, and gated recurrent unit (GRU) neural network, and evaluate their performance in simulating the strain- and stress-controlled triaxial test results of 25 CAC specimens with different mixing proportions and confining pressures. The results demonstrate that the LSTM model, incorporating a 2-time step backward, exhibits superior prediction accuracy and generalization capability compared to other evaluated models, achieving a mean absolute percentage error (MAPE) of 4%. This LSTM model is capable of capturing the stress-strain behavior of CACs across various loading conditions and mixing proportions within a unified framework. Therefore, we suggest that the LSTM model is a promising tool for modeling and analyzing the mechanical behavior of CAC in geotechnical engineering applications.