High turbidity in rivers, intensified by extreme rainfall associated with climate change, poses a great challenge to water resource management globally. To manage and control turbidity events, prediction models have been developed utilizing suspended sediment (SS) data. However, traditional methods for measuring SS data, such as water sampling and laboratory analysis, are time-consuming and impractical for real-time applications. Although the turbidity-SS relationship is widely used, its accuracy depends on sediment particle size distribution. To address this limitation, we developed turbidity-SS equations adaptive to various SS fractions, using data from controlled circulating flume experiments designed to reflect sediment characteristics of a natural river. The results showed significant improvements in the linear turbidity-SS relationship, with R values ranging from 0.60 to 0.99, depending on sediment fractions. These equations were applied to field data from the area upstream of Soyanggang Dam in South Korea, yielding error rates of 1-18%. This study highlights the importance of incorporating sediment fraction variability into turbidity-SS models, which significantly improves their accuracy and reliability. The proposed approach offers a practical and scalable solution for real-time and large-scale SS monitoring, contributing to improved water quality management in various riverine conditions.