The escalating levels of pollution have triggered detrimental impacts on the environment, eliciting a surge in interest regarding combating this predicament. A notable focus has been placed on mitigating the pollution stemming from combustion engine vehicles, driving substantial investments in research and development towards enhancing hybrid and electric vehicle (EV) batteries. The advancements in EV technology hold immense promise in revolutionizing the transportation sector and playing a pivotal role in curbing greenhouse gas emissions. This particular study adds value to the EV industry by accurately forecasting power requirements at designated charging stations and pinpointing the optimal characteristics for these stations. Our innovative approach introduces a refined business process leveraging digital technologies to amplify customer engagement and operational efficacy. By integrating cutting-edge technologies like artificial intelligence (AI) and machine learning (ML), our research strives to tackle challenges associated with EV infrastructure, service gaps in EV facilities, and the ideal power specifications for charging stations. The proposed framework not only carries managerial implications but also underscores the importance of technological readiness, emphasizing the need to efficiently process and interpret extensive datasets while remaining adaptable to environmental variables such as EV availability and renewable energy usage. Despite the obstacles encountered, there lies tremendous potential in developing AI and ML-driven decision support systems tailored to electric vehicle power demands.