Excessive nitrogen and phosphorus pollution, coupled with severe climate change, has significantly intensified surface water eutrophication on a global scale. Accurate water quality assessment is essential for the effective management of eutrophication. This study proposes an integrated MCS-RP-TOPSIS-GSA model for evaluating surface water eutrophication in the Black Soil Region of Northeast China. The Monte Carlo Simulation (MCS) coupled with Random Perturbation (RP) reduced sampling errors and improved simulation authenticity. The relative importance of five key indicators, namely total nitrogen (TN), total phosphorus (TP), permanganate index (CODMn), chlorophyll-a (Chl-a), and secchi disk depth (SD), was evaluated by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Global Sensitivity Analysis (GSA). Furthermore, four typical water types (shallow lakes, deep lakes and reservoirs, marsh wetlands, and rivers) were evaluated using the new water quality simulation model. The simulation achieved the highest accuracy when the coefficient P in the membership function was set to 1.15, with over 50 % of the surface water classified as eutrophic (Q(i) > 60 %, exceeding Class III standards). Moreover, water quality in the west-central region (Q(i)= 63.65 %) was inferior to that in the east (Q(i) = 53.86 %), and water quality of deep lakes and reservoirs (Q(i) = 59.30 %) and marsh wetlands (Q(i) = 53.36 %) was significantly better than that of shallow lakes (Q(i)= 61.46 %) and rivers (Q(i) = 68.39 %). TN, TP and CODMn were identified as the primary sensitive factors influencing surface water eutrophication. Additionally, precipitation and non-point source pollution were the primary drivers of surface water eutrophication in arid and semi-arid regions.