<p>Accurate prediction of sediment transport in tropical rivers remains challenging due to complex geomorphology and strong seasonal variability, particularly in bird-feather-type watersheds. Existing empirical models and discharge-only rating curves often fail to capture these dynamics, creating uncertainty for water-quality-oriented sediment management. This study develops and validates a sensor-based empirical sediment-load estimation framework that integrates hydrometric measurements and morphological information for application in tropical bird-feather-type basins, using the Bomo Watershed in Banyuwangi, Indonesia, as a representative case. Hydrometric parameters, including discharge (Q), water level (H), flow velocity (V), turbidity (NTU), and total dissolved solids (TDS), were coupled with laboratory-derived suspended sediment concentration and repeated bathymetric surveys across wet and dry seasons. Using power-law and multilinear regression, the proposed framework estimates suspended load (qs), bed load (qb), and total load (qt). Strong agreement between empirical and theoretical estimates was obtained for qs and qt, with high correlation (R² = 0.98 in wet-season and 0.79 in dry-season) and MAE = 0.39 ton/day for total sediment load (qt), aggregated across wet and dry seasons, indicating high predictive confidence. In contrast, qb estimates show lower and more variable correlations (R² = 0.42–0.63), reflecting the threshold-controlled nature of bed-load transport during high-flow events. The key novelty of this study lies in combining low-cost sensor-based hydrometric monitoring, bathymetric validation, and bird-feather-type morphometric characterization within a single empirical framework. The resulting approach provides a transferable technological solution for continuous sediment-load estimation to support river normalization, soil and water conservation, and adaptive operation of hydraulic infrastructure in data-scarce tropical watersheds.</p>