This paper explores the application of machine learning methods for the analysis, classification, and forecasting of the dynamics of small reservoirs in Kazakhstan based on long-term satellite observations. The study covers 71 water bodies for which archival satellite imagery from the Landsat 7, Landsat 8, Landsat 9, and Sentinel-2 missions was collected and processed from 2000 to 2024. The water surface area was estimated using the Modified Normalized Difference Water Index (MNDWI) combined with image segmentation algorithms implemented in Matlab, ArcGIS, and Python environments. Based on the derived estimates, time series reflecting seasonal and interannual variations in reservoir surface area were constructed. To identify characteristic types of water-body dynamics, the time series were classified according to seasonal patterns, long-term trends, and interannual variability. In addition, time series analysis was performed using forecasting models, including Prophet and Long Short-Term Memory (LSTM) recurrent neural networks, enabling short-term predictions of water surface area changes for reservoirs of different sizes. The results demonstrate the potential of integrating satellite data with machine learning methods to develop automated monitoring systems and decision-support tools for water resource management in the Republic of Kazakhstan.