More cities are using electric buses, which is an important move to cut down on pollution. supporting sustainable mobility. However, without a solid electric bus charging Infrastructure problems include things like stations not being spread out well, taking too long to charge, and not using resources efficiently. energy use can hinder progress. These problems can cause reduced reliability and. performance of electric bus networks. This paper proposes an AI-based electric bus. A system for planning charging infrastructure that uses machine learning to find the most effective solutions. locations for bus charging stations. We suggest a K-Means clustering approach that looks at big transportation data to find where the most demand is. charging. Our method creates a model by taking into account several factors, such as bus. By looking at routes, how busy the roads are, how many people want to travel, and where the bus depots are, we can spot patterns. Using These patterns can help select the best charging stations to improve accessibility. reduce traffic congestion. The proposed method improves charging locations, waiting times, energy use, and how well it reaches cities. This represents a significant