Traditional econometric models often fail to accurately predict long-term electricity demand due to their inability to adapt to the dynamic nature of energy consumption. This study employs a Multi-Layer Perceptron (MLP) model, integrated with Non-linear Autoregressive Neural Network predictions of population, gross domestic product, electricity access, and urban population, to forecast Ecuador’s electricity demand from 2023 to 2050. Comparative analysis shows that the MLP model significantly outperforms other machine learning models, such as Support Vector Machines and Robust Linear Regression, demonstrating lower prediction errors and higher accuracy. Specifically, the MLP model achieves notably better precision, making it a valuable tool for policymakers and planners aiming to meet future energy needs effectively. The study reveals that electricity consumption in Ecuador could range from 38.74 TWh to 62.1 TWh from 2023 to 2050, underscoring the potential of advanced neural networks to enhance the reliability of electricity demand forecasting in developing countries.