Despite all achievements, and advances in energy markets, microgrids, and smart grids within the world, issues such as power distribution, consumption, or optimization are among the important and significant areas within the industry and technology. As industrialization and technology improve, these subjects become more important. Most of the experts attempt to have far better control on power consumption/distribution, and technologies like combined heat, and power (CHP), or gaselectricity, or demand forecasting, especially in smart sustainable cities (SSCs). Using artificial intelligence (AI) and neural networks (NNs) can have an important role in performing, and optimization that will lead to lowering the issues in future power systems. An NN-long short-term memory (LSTM)-based model can help the experts to control, predict, and optimize the facility consumption, and power distribution. Conceptually, in industrial and SSCs, more they develop, more the quantity of data is going to be generated that a simple and practical tool to research about and analyze these big data is AI. Regarding an outsized amount of data, the training and predicting process of AI is going to be far more accurate, due to the low root mean square error (RMSE). Accordingly, the result is going to be near the actual and help the SSCs to possess controlled power consumption, distribution, and CHPs. Also, the combination of quantum technology with smart grids, and NNs are analyzed. Accordingly, the mentioned technologies cause preventing power loss and promoting a way to a smarter, technology-based, and sustainable world with high ability of demand response (DR).