Energy consumption analysis is essential for maximizing energy use and improving energy efficiency across a range of applications. In this study, we suggest a unique method for analyzing energy use based on real-time data from an energy meter. The information gathered comprises the voltage, current, power, and overall energy consumption’s root mean square (RMS) values. We use the Support Vector Regression (SVR) model, a potent machine learning method known for its ability to handle non-linear connections and high-dimensional data, to reliably forecast future energy usage. A popular interactive computing tool called Jupyter Notebook has the SVR model incorporated, making experimentation and analysis simple. Our proposed methodology proves its effectiveness in anticipating future energy use through testing and evaluation. The SVR-based prediction model’s findings offer useful insights into trends in energy consumption, empowering users to make deft choices that would optimize energy use, cut down on waste, and enhance overall energy sustainability.