In the context of rapid population growth, the expansion of economic sectors, and the accelerated development of digital technologies, the demand for electricity has been increasing significantly, posing new challenges for modern energy systems. In particular, the accurate assessment of electricity consumption, comprehensive analysis of its determining factors, and development of reliable forecasting models have become pressing scientific issues. The aim of this study is to model and forecast key energy and socio-economic indicators within the electricity supply system, using the Kashkadarya region of Uzbekistan as a case study. This research identifies the relationship between electricity consumption and per capita income levels. Additionally, evaluation models aimed at reducing energy losses and improving energy efficiency in the power sector are developed. Models were constructed to determine forecast parameters for regional energy efficiency indicators, network losses, electricity generation, and consumption volumes. The analysis employs correlation and multiple regression methods, while forecasting is conducted using time series models. The empirical results demonstrate a high level of statistical significance of the developed models and confirm the existence of a strong relationship between electricity consumption and the selected explanatory variables. In particular, a 1% increase in per capita income is associated with an average increase of 0,25% in electricity consumption. The findings contribute to improving the accuracy of electricity demand forecasting, enhancing energy management efficiency, and providing a scientific basis for the planning and modernization of energy systems.