This paper examines existing intelligent forecasting approaches, in particular, for solving the problem of evaluating Nuclear Power Plant's (NPP's) energy production. Artificial intelligence (AI) and machine learning (ML) techniques contribute to energy consumption forecasting models. Such models considerably improve the accuracy, reliability, and precision contributing to conventional time series forecasting tools. NPPs do physically produce more or less of the demanded amount of energy. The process of calculating imbalances requires a comparison of electricity purchased or sold under contract with the results of commercial metering of physical production and consumption. This study orients to choose the rational approach by reviewing different ML models for energy prediction. Advanced analytics and AI-enabled algorithms can help identify off-line behaviors to increase efficiency and help balance supply and demand. Better shortterm forecasts can improve power planning by enabling operators to both reduce their dependence on polluting stations and activate an increase in the number of alternating sources. Adjusting the amount of energy produced will lead to energy conservation and an improved environmental situation.