<p>Maintaining ideal water quality is crucial for successful aquaculture and it requires continuous monitoring and classification. Traditional methods of water quality monitoring in aquaculture rely on manual analysis, this leads to time-consuming and prone to subjectivity. Existing deep learning models for water quality classification shows promising, but face limitations in capturing the intricate temporal dependencies within sequential water quality data and ineffective in utilizing computational resources. In this article proposes an Attention-Quantized Gated Recurrent Unit (Attention-Quantized GRU) for water quality classification in aquaculture ponds. Proposed model learning from sequential water quality data, GRU effectively captures temporal relationships within the data, providing a more comprehensive understanding of the aquatic environment. Attention Mechanism focuses on the most informative measurements within a sequence, prioritizing data points with a significant impact on classification accuracy. The attention mechanism can highlight time steps where critical changes occur, giving the model a clearer picture of the overall water quality trend. Quantization reduces memory usage and potentially accelerates computation, making the Attention-Quantized GRU suitable for deployment in resource-constrained environments. Experimental analysis demonstrates that the Attention-Quantized GRU achieves a high accuracy of 99.2% in water quality classification, surpassing the performance of existing models AODEGRU, DSTCNN and LSTM gives accuracy of 94.7%, 92.2% and 90.1% respectively. This improvement can be attributed to the model’s ability to effectively capture temporal dependencies and focus on crucial water quality parameters. The proposed approach empowers aqua culturists with a robust and efficient tool for real-time water quality monitoring and proactive management practices.</p>