According to the present research, the biggest issues that aqua ponds are struggling to deal with include ammonia which greatly impacts the lives of water creatures and the environment. The most typical technique for ammonia concentration measurement has been the discrete sampling and laboratory analysis, which is slow and not effective for real-time ammonia measurements. To overcome the above challenges and improve the monitoring of ammonia pollution in aquaculture systems, we present the Quantum Recurrent Neural Network (Quantum RNN) model in this paper. The proposed Quantum RNNs harbors higher computational efficiency and effective temporal complexity modelling capability in time series datasets through utilizing the quantum computing concepts. The present work will specifically concern with the analysis of how Quantum RNN can be used in predicting ammonia concentrations using aqua ponds data that includes ammonia, temperature, and pH. The present model is thus superior to basic models like RNNs and LSTMs and, in equal measure, less resource demanding. From the experimental results, we found that the proposed Quantum RNN has a better accuracy in predicting the variations of ammonia levels than all the other models for real-time contaminant identification. In this work, we have also provided a realistic approach of enhancing the regulation of water quality in aquaculture to avoid hazardous ammonia concentration.