Outdoor pond aquaculture feeding yields significantly vary with water quality evaluation, estimation, and control. However, several issues challenge the accuracy of water evaluation, such as inexplicable missing data in acquired parameters and strong coupling and time-lag correlations among multiple parameters. Inaccurate water evaluation results further introduce errors into the estimation and control processes, potentially leading to sudden losses in aquaculture. Therefore, a real-time water parameter monitoring device is firstly designed in this research, featuring an ESP32 microcontroller and an embedded MATLAB application. This device allows real-time data on ammonia nitrogen, dissolved oxygen, pH value, water temperature, and water depth to be transmitted to the cloud platform, specifically the OneNet IoT platform. Based on the device design, an innovative method with VMD-LSTM-XGBoost structure for parameter decomposition and reconstruction to extraction of temporal information among parameters and the supplementation of missing data. Meanwhile, the Sparrow Search Algorithm (SSA) is employed for decomposition numbers optimization. Furthermore, the combination of AHP-CV-normal cloud model is designed to improve the accuracy of water quality evaluation. Finally, an integrated learning model is constructed to improve the accuracy of water quality prediction. This research optimized the decomposition of 4 parameter groups into 34 sets of time-series data based on collected data and completed missing parameter supplementation. The experimental validation shows that the proposed AHP-CV-normal cloud model for water quality assessment achieves a classification accuracy rate of over 98%, demonstrating good feasibility. The designed VMD-LSTM-XGBoost hybrid model achieves a test accuracy of 96.209% on the validation set, demonstrating strong predictive performance. This research provides an effective solution for monitoring water quality parameters, data imputation, water quality assessment, and prediction in the complex environment of outdoor pond aquaculture, offering theoretical support for feeding strategies.