Forecasting river water quality helps water managers prepare for suspended sediment spikes that can affect drinking water supplies and ecosystem health. This study presents a modeling framework that uses machine learning to combine data from different tributaries in a watershed to predict downstream turbidity. By tailoring models to local watershed conditions and optimizing them for both accuracy and efficiency, this approach aims to support real-time decision-making in nested and complex river systems.