The growing reliance on Unmanned Aerial Vehicles (UAVs) necessitates efficient energy management to ensure optimal performance and longevity. This study investigates the energy consumption patterns and efficiency degradation in DJI Mini 2 drone batteries by the utilization of a deep neural network (DNN) for predictive analysis. The experimental work conducted repeated flight tests and monitoring battery discharge from 100% to 27% over 20 trials. The testing conditions, including flight duration and environmental factors, were controlled to ensure repeatability and to minimize any external influences on the recorded data. These data were stored onto AIRDATA and then recollected for new labeling. The initial flights demonstrated stable performance, while subsequent flights showed a gradual reduction in flight time which indicated performance degradation. To ensure consistent power usage and minimize external influences, hover mode was selected for all flights. The DNN was trained on this data and employed metrics of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient of Variation of the Root Mean Squared Error (CVRMSE), and determination coefficient (R²). The proposed model achieved an MSE of 0.352%, RMSE of 0.593%, MAE of 0.324%, MAPE of 0.857%, CVRMSE of 0.743%, and R² of 0.981. These results demonstrate the DNN's ability to accurately predict future power consumption that in turn provides insights for energy management and extending battery life. This research contributes to the development of sustainable UAV operations by enhancing understanding of battery performance under real-world conditions.