Effective energy management in industrial facilities is crucial for achieving sustainability and operational efficiency. This thesis presents an advanced framework consisting of two complementary components: a predictive energy modeling system and the development of a real-time visualization application for facility management. The research is focused on enhancing energy forecasting capabilities and providing practical visualization tools to improve decision-making processes. The first component of the study involves the development of machine learning models to forecast energy consumption in the PCMA San Benigno facility located in Turin Italy, which is analyzed as a single energy-consuming entity. The models developed include a Fully Connected Neural Network (FCNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (Bi-LSTM).