The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumption patterns. Machine learning has emerged as a powerful tool, particularly with the latest developments in deep learning. Such tools can predict electricity demand and, thus, contribute to better decision-making by energy managers. However, it is important to recognize that there are no efficient methods for forecasting peak demand growth. In addition, features that add complexity, such as climate change and economic growth, take time to model. Therefore, these new tools can be integrated with other proven tools that can be used to model specific system structures, such as system dynamics. This research proposes a unique framework to support decision-makers in dealing with daily activities while attentively tracking monthly peak demand. This approach integrates advances in machine learning and system dynamics. This integration has the potential to contribute to more precise forecasts, which can help to develop strategies that can deal with supply and demand variations. A real-world case study was used to comprehend the needs of the environment and the effects of COVID-19 on power systems; it also helps to demonstrate the use of leading-edge tools, such as convolutional neural networks (CNNs), to predict electricity demand. Three well-known CNN variants were studied: a multichannel CNN, CNN-LSTM, and a multi-head CNN. This study found that the multichannel CNN outperformed all the models, with an R2 of 0.92 and a MAPE value of 1.62% for predicting the month-ahead peak demand. The multichannel CNN consists of one main model that processes four input features as a separate channel, resulting in one feature map. Furthermore, a system dynamics model was introduced to model the energy sector’s dynamic behavior (i.e., residential, commercial, and government demands, etc.). The calibrated model reproduced the historical data curve fairly well between 2005 and 2017, with an R2 value of 0.94 and a MAPE value of 4.8%.