With growing environmental concerns and the urgent need to mitigate global warming, there is a significant push towards adopting renewable energy sources, such as solar and wind power, which are crucial for reducing reliance on fossil fuels. However, the inherent variability of these sources presents substantial challenges for effective energy management, especially in the industrial sector. This research, focusing on the meat processing industry, adopts a two-pronged approach to tackle these issues. It starts by using a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) to analyze open-source weather data, aiming to predict the impact of weather variations on renewable energy production. This predictive effort is crucial for enhancing the reliability of renewable sources in industrial applications. Despite the advancements in forecasting, the variable nature of energy supply necessitates efficient management strategies. Therefore, the study implements a Fuzzy Logic system to manage electricity consumption based on real-time energy availability and demand within the meat processing industry. Chosen for its robustness in handling uncertainty, Fuzzy Logic enables more informed decision-making under ambiguous conditions, thereby reducing reliance on conventional energy grids and improving energy use efficiency. This dual strategy aims to foster more sustainable and environmentally friendly industrial operations, addressing both the variability of renewable energy sources and the challenges in energy consumption management.