The Internet of Things (IoT)-based smart solutions have been developed to predict water quality and they are becoming an increasingly important means of providing efficient solutions through communication technologies. IoT systems are used for enabling connection between various devices based on the ability to gather and collect information. Furthermore, IoT systems are designed to address the environment and the automation industry. The threats associated with aquaponics farming are managed through an IoT-based smart water monitoring framework, which has become increasingly relevant in recent days. Therefore, this approach is crucial for achieving a remarkable improvement in order to increase the productivity rate and yield. The quality of water directly affects the rate of growth, efficiency of feed, and the overall health rate of the fish, plants, and bacteria. Insufficient knowledge about species selection poses a significant challenge in aquaponics farming, as it heavily relies on the water quality parameters. To address the challenges of conventional models, we have developed an effective IoT-based water quality prediction model, more specifically designed for aquaponic fish ponds. The data needed to perform the developed water quality prediction model will be acquired from "a simple dataset of aquaponic fish pond IoT" database. After that, these data are forwarded to the feature extraction phase. The weighted features, DBN (Deep Belief Network) features, and the original features are achieved in the feature extraction stage. The weighted features are obtained using the Revamped Fitness-based Mother Optimization Algorithm (RF-MOA). Subsequently, these extracted features are fed into the Multi-Scale feature fusion-based Convolutional Autoencoder with a Gated Recurrent Unit (MS-CAGRU) network for predicting the water quality. Thus, the water quality predicted data is obtained. The proposed model integrates GRU networks with a convolutional autoencoder to improve water quality prediction by capturing trends and managing temporal dependencies. It enhances accuracy by analysing key parameters and employing techniques to reduce overfitting. The effectiveness of the proposed system is evaluated in comparison to the traditional models using some evaluation measures.