Accurate load forecasting helps optimize energy management systems by estimating energy demand. CatBoost, a sophisticated gradient boosting algorithm, is used in clouds to improve energy management load predictions. This strategy uses CatBoost's enhanced skills in processing massive datasets and modeling complicated connections in time-series to improve energy demand projections. Energy suppliers should reduce costs, ensure system stability, and make smart resource allocation choices. Scalable and accessible cloud deployment allows real-time data processing and interaction with energy management systems. This technology might change energy forecasting by improving accuracy and reliability, allowing better energy management solutions for sustainable development. Taken from Load_Data_Metrics 5 areas and 5 parameters are sampled. Average Load (MW): 70.64-103.62, Peak Load (MW): 159.39 277.06, Off-Peak Load (MW): 60.15-93.16, Variance (MW²): 265.22-388.57, Min Load (MW): 13.95-35.84. Forecast_Accuracy data. Five test scenarios with five parameters are sampled. Results: RMSE (MW): 3.09-9.65, MAE (MW): 1.68-4.89, MAPE (%): 2.29-13.2, R² Score: 0.867-0.986, Training Time (s): 17.96-40.9. Alert_Trends samples 5 areas and 5 parameters. Alerts Generated: 23–44, Severity (1–10): 1–6, Average Resolution Time (min): 8.01–20.88 Morning, afternoon, evening, and night are peak alert times.