Demand prediction plays a crucial role in the real-time operation of electrical systems, particularly for monitoring, planning, and optimizing the operation of electrical devices. Accurate demand prediction ensures effective coordination between consumers and power companies, which is essential for efficient power grid management. This paper presents a novel approach for energy demand prediction using a neural network combined with an optimization-based method. Initially, a conventional neural network is employed to predict the required energy demand based on historical data. However, to improve prediction accuracy, a genetic algorithm (GA) is introduced to adjust the neural network’s weights automatically. This optimization method fine-tunes the network, enabling it to achieve better performance in predicting short-term energy demand. The integration of the genetic algorithm helps in overcoming the limitations of traditional training methods, such as slow convergence or local minima. Experimental results, based on real-time data randomly selected from various sources, demonstrate that the genetic algorithm-based neural network outperforms conventional approaches in terms of prediction accuracy and computational efficiency. The proposed method is validated through extensive testing, showing its potential for accurate short-term load forecasting in dynamic and complex energy systems. This research highlights the effectiveness of optimization algorithms in enhancing the predictive power of neural networks for energy demand forecasting.