The extensive implementation of electric vehicles (EVs) and hybrid vehicles (HVs) is important to decrease dependence on fossil fuels and mitigate greenhouse gas emissions, which is a crucial measure toward achieving sustainable transportation and climate objectives. Our research utilizes a high-dimensional clustering approach to analyze the comprehensive travel and charging patterns of EV and hybrid vehicle (HV) users’ behaviors. This approach is crucial due to the complex nature of user behaviors and the impact of seasonal variations. In this study, we employed a dataset comprising more than 70 vehicles in Canada. To get insights into various user behaviors across different environmental situations, we employed Agglomerative Hierarchical Clustering (AHC), K-Means, Deep Embedded Clustering (DEC), and DBSCAN algorithms. The results showed clear clusters that demonstrated different travel and charging patterns, especially between warm and cold months. Particularly, in warm months, this study identified three main clusters for EVs that focused on urban, mixed, and long-range travel behaviors. On the other hand, in the cold months, there was a shift toward more frequent urban travel and modified charging routines. In conclusion, our research highlights the importance of adaptive charging infrastructure and legislative frameworks that accommodate the varied requirements of EV and HV users, considering their evolving behaviors throughout the year, to facilitate the shift toward environmentally friendly transportation.