K. Madhavi, S. Rajesh, R. Maliklal Naik, B. Naveen 02 08 India is rapidly transitioning toward the electrification of public transportationparticularly electric busesto reduce carbon emissions and dependency on fossil fuels As the countrys energy demands continue to grow electric buses have emerged as a key solution for sustainable urban transport By 2022 India had more than 4000 electric buses in operation a significant increase from just a few hundred in 2017 This growth reflects the nations strong commitment to reducing pollution and improving energy efficiency To further enhance operational performance a datadriven approach using machine learning is being applied to predict energy consumption and optimize economic performance in electric city buses This approach aims to improve the overall efficiency and sustainability of urban public transportation systems Traditionally electric bus management relied on static route planning manual scheduling and historical data making the system reactive rather than proactive Maintenance and energy optimization were often performed only after issues occurred leading to inefficiencies and increased operational costs These systems lacked predictive analysis resulting in inefficient energy use suboptimal route planning and unpredictable expenses As urban centers face rising energy demands and environmental pressures there is a clear need for intelligent dynamic fleet management A machine learningbased system can analyze realtime data from electric busesincluding battery levels route patterns and weather conditionsto accurately predict energy consumption and operational costs This enables dynamic route optimization predictive maintenance and load balancing significantly reducing energy waste and operational costs while enhancing the efficiency of public transport systems Additionally AIdriven models will provide realtime insights allowing fleet operators to make proactive decisions that improve overall performance and reliability Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026