Reservoir sedimentation is a gradual accumulation of the incoming sediment load from a river; it is important to predict sediment inflow at Tekeze dam watershed and evaluate its consequences on the reservoir and sustain the reservoir through long term optimum sediment management program. The overall goal of this study focuses on predicting of sediment yield and identification of most appropriate sediment management strategy by using SWAT model with GIS interface in the Tekeze Basin, Tekeze Reservoir watershed. Tekeze Reservoir Dam is located on Tekeze River (Tekeze Basin) approximately at 13° 21’ North and 38° 45’ east, approximately 80 km west of the town of Mekele. The model is calibrated and validated for both flow and sediment concentration at Embamedere station and run at Tekeze dam reservoir outlet (28,916.53km2) to estimate the sediment yield. Flow calibration gives coefficient of determination (R2) and Nash-Sutcliffe simulation efficiency (ENS) 0.84 and 0.73 respectively. Flow validation gives 0.74 and 0.71 for R2 and ENS values respectively. Sediment calibration gives R2 and ENS 0.78 and 0.77 respectively and validation test gives R2 and ENS 0.69 and 0.69 respectively. This result indicates that the observed values show good agreement with simulated value for both flow and sediment yield. In this study the SWAT model yields average annual sediment flow of 2846 ton/km2/yr. or (28.46 ton/ha/yr.) at Tekeze dam outlet site. Identifying erosion prone areas in the watershed enables the watershed management to be applied to the proper areas to reduce the sediment yield. Sub basin 19, 10, 24, 25 and 27 ranks first to fifth according to their Sediment yield respectively and are found to be erosion prone areas. For proper management of the watershed, from the scenarios developed, changing 50% of Pasture land to forest mixed reduces 22% of sediment volume and taken as best management method. Moreover, applying terracing and filter strip in low slope areas reduces 37% and 11% of sediment volume respectively and could give potential effect of best management practice.