As modern living and industrial spaces grow more complex, the global demand for electricity is reaching levels we have never seen before. Traditional ways of managing energy—which usually involve manual checks or simply waiting for a monthly bill—are no longer effective in our fast-paced world. This research introduces a smarter way to manage power by using Machine Learning to turn raw electrical data into helpful, real-time insights. By using a Random Forest model trained on the Kaggle Household Electric Power Consumption dataset, our system achieved a predictive accuracy (R2 score) of 0.91. We built a modular solution featuring a smooth web dashboard, a Flask- powered bridge, and a high-speed prediction engine. The result is a tool that helps people see exactly where their energy goes and how to save it, providing answers in less than a second to support a more sustainable and budget- friendly future.