Hydropower generation plays a crucial role in the global energy landscape, offering a renewable and sustainable source of electricity. Accurate forecasting of hydropower output is essential for efficient energy management and maintaining grid stability. This paper presents an autoregressive Long Short-Term Memory (LSTM) model designed to predict short-term hydropower production, specifically targeting the hourly water output decisions of two interconnected hydropower plants located on the Péribonka River in Québec, Canada. Given the critical role of efficient scheduling in hydropower operations, especially within the Short-Term Hydropower Scheduling (STHS) problem, our model aims to offer a viable machine learning-based solution to complement traditional optimization approaches. We evaluated the LSTM model by comparing its predictive performance with historical operational data and results derived from a deterministic Mixed-Integer Linear Programming (MILP) model. Our analysis covers multiple validation instances, showcasing the capabilities of the model and highlighting its strengths and limitations. The results demonstrate that the autoregressive LSTM approach successfully captures the underlying patterns in water discharge decisions, providing predictions that are generally aligned with operational realities and optimized benchmarks. However, the study also underscores challenges such as maintaining reservoir volume constraints, particularly in periods of high inflow variability. Despite these challenges, the LSTM model presents promising predictive performance, laying the foundation for further improvements in integrating machine learning into short-term hydropower management. To our knowledge, this is the first study to apply an autoregressive supervised LSTM model to predict hourly water flow decisions in hydropower systems, thus significantly contributing to the advancement of machine learning applications in hydropower~scheduling.