Electricity consumption is a vital index significantly impacting the efficient operation of power transmission systems. This research explores the correlation between external causality, such as urban population, and electricity demand increase by processing relevant data and constructing models, offering insights for long-term electricity demand estimation and related network planning formulation at the national level. By conducting feature extraction and analysis, this study reveals the inner correlation between these variables using Pearson and Spearman correlation coefficients, regardless of the disparities among different countries. Utilizing statistical modeling, this research analyzes data on electricity demand growth across 25 developed regions. After integrating the data, an applicable regression model – XGBoost Regressor is developed to predict national electricity consumption, incorporating evaluation metrics for comparison and accuracy validation. Compared with other models, the forecasting of the XGBoost Regressor is more stable, achieving satisfactory accuracy on the given samples without overly biased predictions.