In light of the challenges posed by groundwater contamination and the urgent need for accurate and efficient groundwater contaminated source estimation (GCSE), the present study proposes a novel approach for GCSE using a deep adaptive cycle generative adversarial neural network (DA-CGAN). Given the equifinality from different parameters (EFDP) often associated with GCSE, we leveraged a bidirectional adversarial training pattern involving a forward process and a recovery process to supervise the inverse mapping relationship. Once trained, the forward process can be utilized to provide estimation for GSCE. This bidirectional-training strategy mitigates EFDP, thereby effectively enhancing the reliability of GCSE. Moreover, the performance of DA-CGAN is closely related to the quality of the training samples. To address this, we introduced a significant enhancement through an adaptive sampling strategy. This substantially improves the quality of training samples and consequently increases the accuracy of the GCSE. Furthermore, the inherent data-driven attribute of the deep cycle GAN considerably reduces computational costs when conducting GCSE. The research unfolds in the contexts of both hypothetical and real-world scenarios, with the goal of providing an efficient, precise, and cost-effective solution for GCSE. The results demonstrate that the DA-CGAN, an innovative model in the hydrogeological domain, exhibits superior performance in both estimation accuracy (Average Relative Error (ARE) of 4.91% and R of 0.998) and computational efficiency (0.17 seconds per run). This is particularly notable when compared with typical inverse methods such as the genetic algorithm (GA) and the ensemble kalman filter (ENKF).