In this study, convolutional neural networks (CNN) and statistical methods are used to optimize pump performance and reliability in drinking water wells. An evaluation of seasonal variations in pump efficiency and operational characteristics was conducted over a four-year period in Bandar Torkaman, Iran, using deep learning and probabilistic analysis. To extract features from time-series pump data, a multi-layer CNN architecture was developed using 32 and 64 layers of filters. The model displayed significant seasonal variations, with optimal accuracy in winter (R2 = 0.95, MABE = 14.60, RMSE = 39.4 in January) and reduced reliability in summer (R2 = 0.54, MABE = 171.60, RMSE = 386.9). In 10 out of 12 months, the Weibull distribution best characterized water levels, whereas the Lognormal distribution dominated flow rate patterns in 7 months, revealing system behavior. Winter operations achieved 4.42% efficiency and minimal energy consumption (3.15 kWh/m3) compared to summer operations, which achieved 0.70% efficiency and 24.31 kWh/m3. According to the study, optimal operating parameters include flow rates of 7.5-8.5 m3/h, water levels of 45-65 meters, and seasonally-adjusted RPM ranges (1100-1200 RPM in winter, 2350-3040 RPM in summer). These findings offer valuable insights for optimizing pump operations in regions with significant seasonal variations, as well as highlighting the importance of adaptive management strategies and season-specific predictive modeling approaches to enhance system performance and energy efficiency.