In response to the rapid growth of electric vehicles (EVs), our research focuses on the critical need for efficient management of charging infrastructure to facilitate the widespread adoption of EVs. Thisresearch leverages historical charging data as a foundation for predicting charging station availability. The focus of our approach lies in the utilization of autoregressive models and Long Short-Term Memory (LSTM) algorithms, which play a crucial role in enhancing the accuracy of our predictions. We combine the results to build the ensemble model, improving prediction accuracy. Additionally, we enhance our model by incorporating transfer learning (TL) techniques to adapt it to new stations. The resulting predictive methodology empowers users with accurate insights into charging station availability one hour in advance, serving as a valuable tool for effectively planning EV charging activities.Our research goes beyond mere forecasting; it aims to contribute significantly to optimizing EV usage. By supporting informed decision making and encouraging efficient charging practices, we strive to pave the way for the seamless integration of EVs into modern transportation systems. Through these efforts, our research seeks to advance sustainable and eco-friendly mobility solutions in the realm of electric transportation.