Dr.Amita Johar1, Bobbala Bhavana2, Annarapu Meghana3, Chavva Vishwateja4, G Arpan Varma5 03 06 The electrification of transportation especially city buses is becoming increasingly important for achieving sustainable mobility and reducing environmental impact However designing and operating battery electric buses BEBs efficiently requires a deep understanding of realworld driving conditions including variations in speed passenger load traffic congestion and route characteristics One of the major challenges in this domain is accurately predicting energy demand as uncertainties often lead to overly conservative system designs resulting in higher costs oversized batteries and reduced operational efficiencyThis study addresses these challenges by introducing a set of novel explanatory variables that effectively capture speed patterns and driving behavior These variables provide a more detailed representation of realworld conditions and are integrated into advanced machine learning models to enhance prediction accuracy A total of five different algorithms were developed and evaluated considering factors such as prediction accuracy robustness computational efficiency and practical applicabilityThe experimental results demonstrate that the proposed models achieve prediction accuracy above 94 indicating their strong capability in modeling complex energy consumption patterns Additionally the approach reduces reliance on complex physical simulations making it more scalable and suitable for realtime applications This enables fleet operators to make better decisions regarding route planning battery sizing and charging infrastructure placement Overall the proposed methodology contributes to cost reduction improved operational efficiency and supports the wider adoption of clean and sustainable public transportation systemsFurthermore the proposed framework can be easily adapted to different cities and operational scenarios making it highly flexible for future deployments It also opens opportunities for integrating realtime data analytics and smart transportation systems enabling continuous monitoring and optimization of energy usage This adaptability ensures longterm benefits in evolving urban mobility environments Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026