Accurate forecasting of reservoir levels in hydroelectric power plants is essential for efficient energy generation, operational safety, and sustainable water management. This study proposes a hybrid forecasting framework that integrates Seasonal-Trend Decomposition using Loess (STL) with the Neural Hierarchical Interpolation Time Series (NHITS) model optimized through Multi-Agent Hyperparameter Optimization (HPO). The STL filter is employed to remove high-frequency noise and preserve underlying signal trends, enhancing the quality of inputs for the predictive model. The NHITS architecture leverages hierarchical multi-scale processing and interpolation-based reconstruction to capture both short- and long-term temporal dependencies, while the Multi-Agent HPO framework ensures optimal hyperparameter configuration through cooperative agent-based exploration. The proposed method was evaluated using turbine flow data from the Santo Antônio Hydroelectric Power Plant in Brazil, achieving superior performance compared to state-of-the-art benchmarks, including transformer-based and recurrent models, across very short- and short-term forecasting horizons. The model demonstrated substantial reductions in RMSE, MAE, MAPE, and MSLE, while maintaining competitive inference times. The results confirm that the STL–NHITS architecture optimized via Multi-Agent HPO offers an efficient, robust, and interpretable solution for hydropower forecasting, providing a valuable tool for intelligent management of renewable energy systems.