India’s industrial sector contributes significantly to national electricity demand, yet existing systems lack a unified approach to prediction, risk detection, and anomaly identification. This paper presents the India Energy Intelligence Platform (IEIP), an integrated machine learning framework designed to analyze industrial electricity consumption across multiple Indian states and sectors. The system is built using a dataset of 100,000 industrial records with features such as employees, production capacity, environmental conditions, and renewable energy supply. Four machine learning techniques are employed: Linear Regression for consumption prediction, Gradient Boosting for shortage risk classification, K-Means clustering for behavioral segmentation, and Isolation Forest for anomaly detection. The models achieve strong performance, with prediction accuracy (R² ≈ 0.79) and classification accuracy of 97.12%. The system is deployed using an interactive Streamlit dashboard providing real-time insights for policymakers and industry stakeholders. The proposed framework offers a scalable and practical solution for intelligent energy management and decision-making
P et al. (Wed,) studied this question.