Water quality degradation poses an increasing challenge for the sustainable management of surface water resources in Iran's semi-arid regions. This study employed an integrated approach, combining the Shannon's Entropy Water Quality Index (EWQI), Monte Carlo simulation (MCS) and time series forecasting models, to evaluate and forecast the water quality dynamics in the Dez Dam reservoir in Iran. Monthly water quality data (2016-2025) from three monitoring stations were analyzed for key physicochemical parameters. The EWQI ranged from 82 to 157, corresponding to poor to excellent water quality classes, with nutrient enrichment emerging as the dominant factor influencing water quality variability. Monte Carlo simulations quantified uncertainty in parameter weights and yielded narrow 95% prediction uncertainty bands (d-factor < 0.5), confirming the model's robustness. To forecast EWQI one year ahead (April 2025-March 2026), four deep learning (DL) architectures including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Simple Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN) were benchmarked against a Seasonal ARIMA model. Among these, LSTM achieved the best accuracy (RMSE = 2.05, NSE = 0.93, KGE = 0.88), effectively capturing temporal dependencies and non-linear trends. Forecast results indicate that the reservoir will maintain a "medium" water quality status, emphasizing the need for continued nutrient control and adaptive management strategies. The proposed EWQI-MCS-DL framework offers a reliable and transferable approach for water quality assessment and forecasting in dam reservoirs under environmental change.