This study examines the design and management of energy communities in rural environments, where the geographic dispersion of households and the limitations of conventional electrical infrastructure make local networks a more viable and sustainable solution. Energy communities enable residential and commercial users to take on a dual role as consumers and prosumers, maximizing the integration of renewable sources such as solar and wind energy. However, the inherent variability of these sources, combined with demand patterns, introduces significant technical challenges in terms of system stability and efficiency. The proposed methodology is structured in two main phases. In the first phase, a rural energy community will be modeled using Simulink, taking into account the consumption and generation profiles of each household, as well as their geographic distribution, to incorporate conductor loss factors and regulatory constraints on voltage, current, and power. This model will generate databases representing energy generation and consumption over a daily cycle. The resulting analysis will consider current regulations and seek to optimize energy self-sufficiency by minimizing dependence on the conventional power grid and maximizing the utilization of surplus energy from interconnected households. The study integrates simulations with machine learning techniques to predict grid intensity one hour in advance, aiming to mitigate overcurrent occurrences and enhance the system’s operational stability.