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The rapid expansion of electric vehicle (EV) adoption has led to a swift growth in charging infrastructure, accompanied by a rising incidence of malfunctions in charging equipment. To enhance the management and maintenance of these facilities, this study introduces an approach using the XGBoost algorithm, specifically tailored for direct current (DC) EV charging stations. A comprehensive operation and maintenance framework is developed, grounded in thorough condition evaluation. This framework incorporates the current functional state of the stations alongside patterns in user charging behavior. Key considerations include drivers’ traveling and recharging preferences, prevailing road congestion levels, and the condition of charging hardware. The model is trained on a dataset compiled from records, live surveillance information, and ambient external conditions. Employing the XGBoost technique, a robust evaluation model assesses the operational health of charging stations. Drawing from these evaluation outcomes and integrating relevant fault-related metrics, a dedicated risk evaluation framework is formulated. By using the energy not charged (ENC) metric, the system performs ongoing risk monitoring for each facility, assigns risk-priority rankings, and establishes a sequence for necessary interventions. An advanced optimization model for DC station maintenance is then constructed, balancing financial efficiency with system reliability objectives, ultimately generating an effective maintenance schedule across stations. Case study findings indicate that this combined condition assessment and strategic maintenance plan markedly enhances overall system dependability and delivers superior economic returns from upkeep efforts, all while satisfying established performance criteria.