Monitoring water quality is one of the considerable topics in aquaponics, as the imbalance in pH level, the dissolved oxygen (DO), and nutrient concentrations can negatively affect plant growth and other living beings. The traditional approaches show numerous challenges due to manual intervention, making the process become labor-intensive and prone to human error. Therefore, this study proposes the unique architecture called CNN-LSTM-RF, which is the combination of Convolution Neural Network, Long-Short Term Memory, and Random Forest. While the CNN allows for the filtering process that retains the most prominent features among windows to be transformed and fed to the LSTM to learn the relationship of features in temporal series, the Random Forest serves as the final classification to predict the final output for the pump state. As a result, the proposed model can outperform most of the other deep learning configurations in water quality prediction, reaching up to 62.6% accuracy. The system powered by such a deep learning model shows its dynamical adaption to environmental changes, optimizing water conditions with minimal human input.