<p>Water quality has become a major issue in the maintenance of aquatic ecosystems and healthy breeding of fish. Proper water quality classification is necessary to monitor the key environmental changes, to support the practice of aquaculture, and to minimize the mortality of fish. Due the complexity of water quality data, with several correlated parameters and the water parameters changes over time.The traditional machine learning algorithms cannot capture such nonlinear dependencies, and even the current deep learning algorithms face issues of overfitting, slow convergence, and low computational costs which restricts their ability to perform reliably in a real-time application. To address these issues, this paper presents a Quantum Convolutional LSTM (QCLSTM) framework to combine quantum-inspired feature extraction with inference of temporal sequences. The suggested method is trained on Kaggle’s “pondsdata” and is efficient in terms of both capturing the complex interactions between features and capturing the long-term relationships in water quality data. Experimental analysis has indicated that the model has an accuracy of 98.72%, a loss of 2.91, a precision of 99.10%, a recall of 99.10%, and a F1-score of 99.10%, which is always better than other traditional deep learning methods Convolutional Neural Network combined with Long-ShortTerm Memory (98.21% accuracy), Gated Recurrent Unit (95.38% accuracy), and Artificial Neural Network (95.77% accuracy) in factors such as accuracy, generalizability, and cost-effective computation. The higher level of performance, lower overfitting, and faster convergence are the indicators of the strength of the proposed method. Such results make it a credible and understandable instrument of sustainable aquaculture management and real-time water quality control, which eventually leads to healthier ecosystems and lower mortality of fish.</p>