In the dynamic shift towards a sustainable global energy landscape, electric vehicle (EV) charging stations emerge as crucial components, fostering clean mobility while holding untapped potential for grid support. This study investigates the flexibility potential of charging stations, aiming to provide ancillary services to the grid. The methodology, encompassing diverse factors from parking scenarios to technical regulations, serves a dual purpose: probabilistically forecasting the station’s engagement in services and assessing its capacity to mitigate grid events. Building on prior research in frequency maintenance and voltage stability, the methodology creates a comprehensive framework, challenging the synthesis of this knowledge with the dynamic nature of EV charging patterns. Leveraging a two-year dataset from a specific charging station, the research builds upon previous statistical analyses conducted in a prior study. In this continuation, the study employs a variety of statistical analyses and machine learning models, including XGBoost, to explore time-based, energy-based, and behavior-based perspectives. Results showcase increased charging sessions and energy consumption, emphasizing the growing role of EVs in the energy landscape. The study presents simulation algorithms for ancillary service potential, revealing the impact of a EV charging management proposal on daily power consumption, client satisfaction, net profit, and pricing dynamics. The intricate interplay between charging stations, electric grid providers, and users underscores the need for strategic considerations to optimize economic viability and user satisfaction in the evolving electric vehicle charging landscape. As the electric mobility era unfolds, this study provides a crucial approach for forecasting capabilities and estimating economic gains, contributing to the sustainable integration of electric vehicles into the global energy ecosystem.