Aquaculture is a vital contributor to global food security, yet maintaining optimal water quality remains a persistent challenge, particularly in resource-limited rural settings. This study integrates Internet of Things (IoT) technology, Machine Learning (ML) models, and the Quantum Approximate Optimization Algorithm (QAOA) to enhance water quality monitoring and prediction in aquaculture. IoT sensors continuously measured parameters such as temperature, dissolved oxygen (DO), pH, and turbidity, while ML models—including Random Forest—provided high accuracy predictions (R2 = 0.999, RMSE = 0.0998 mg/L). The integration of the QAOA reduced model training time by 50%, enabling rapid, real-time responses to changing water conditions. Over 6000 corrective interventions were conducted during the study, maintaining fish survival rates above 90% in tropical aquaculture environments. This adaptable system is designed for both urban and rural settings, using low-cost sensors and local data processing for constrained environments or cloud-based systems for real-time analysis. The results demonstrate the potential of IoT–ML–QAOA integration to mitigate environmental risks, optimize fish health, and support sustainable aquaculture practices. By addressing technological and infrastructural constraints, this study advances aquaculture management and contributes to global food security.