Water quality is a critical determinant of productivity and ecosystem health in aquaculture, yet effective management is frequently challenged by dynamic environmental fluctuations. This research introduces an Internet of Things (IoT)-to-Cloud framework for monitoring and predicting the Water Quality Index (WQI) by integrating large-scale, heterogeneous sensor data. A comprehensive dataset was constructed, comprising 135,783 rows of synchronized water parameters collected from both extensive (naturalistic) and intensive (controlled) aquaculture systems in Bangladesh. The study establishes a rigorous computational benchmark by evaluating six machine learning (ML) classification architectures: Decision Tree, AdaBoost, Gradient Boosting, Extra Trees, Support Vector Classifier, and Random Forest. To enhance model reliability and predictive consistency, advanced preprocessing techniques were applied, including standard scaling and comparative environment analysis. The Random Forest Classifier achieved the highest performance, with accuracies of 99.56% and 99.51% in extensive and intensive environments, respectively. Notably, isolating these environments resolved previous metric inconsistencies and produced robust $$ scores exceeding 0.98. These results offer a high-precision methodology for aquaculture surveillance, providing an early warning system to improve fish survival rates and promote sustainable fisheries management.