Machine Learning is revolutionizing energy forecasting by providing precise long-term demand estimates, enhancing energy supply reliability, and ensuring efficient energy generation as well as power system demand prediction. This chapter develops an artificial intelligence (AI) approach to integrate energy technology and demand forecasting seamlessly. We explain three ML models to predict annual energy demand by implementing an accurate energy generation system. The employed models are Multiple Linear Regression, Artificial Neural Network, and Random Forest algorithm (RF). We have implemented the mentioned models on real-world datasets collected from various parameters over five years from weather stations to achieve precision. We observe that the RF algorithm performs the best among the employed algorithms by reaching 92.41% accuracy. Besides, some common metrics including Mean Square Error, Root Mean Square Error, and Mean Absolute Error, are used to evaluate the model performance. To reduce the negative consequences of global warming brought on by energy generation, our proposed model shows accuracy and dependability in predicting future energy demand. This chapter aims to improve efficiency and advance energy planning in national grid power system management through the application of machine learning techniques.