Aqua status quality prediction is a vital part of environmental monitoring, with significant implications for public health, ecosystem sustainability, and Aqua resource management. Traditional methods for evaluating aqua quality, is like taking the manual sample and to perform the laboratory analysis, are often labour-intensive and limited in scope. Recent developments in deep learning have transformed this domain by empowering the expansion of predictive models accomplished with analysing non-linear relationships in Aqua quality. Hybrid deep learning models, merging Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and Gated Recurrent Units (GRUs), have verified superior performance in apprehending spatial and temporal dependencies in Aqua quality data. Optimization algorithms such as Particle Swarm Optimization, Grey Wolf Optimization, Sparrow Search Optimization (SSO), and Beluga Whale Optimization (BWO) have been integrated to enhance model accuracy and efficiency. Attention mechanisms and feature selection techniques have further improved model performance, while the integration of IoT has enabled real-time monitoring, addressing the limitations of traditional methods. Despite these advancements, challenges related to model interpretability, computational complexity and most important part data availability remain as it is. This review explores the pragmatic augmentation in hybrid deep learning models for Aqua quality prediction, focusing on their architecture, optimization techniques, and real-world applications.