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Optimizing condition-based maintenance (CBM) strategies based on machine learning (ML) methods as reinforcement learning (RL) have been receiving increasing attention due to their competencies. However, most existing research depends on simplifying assumptions about the deterioration process and aims to obtain only a threshold for preventive maintenance using the maintenance cost as RL's reward function. To tackle these limitations, this paper proposes a data-driven CBM optimization methodology that combines ML prediction model and RL method with a reliability-based method for remaining useful life (RUL) estimation. The prediction model learns the system's actual deterioration from historical deterioration data. The RL method integrates the prediction model with a customized reward function that minimizes the average maintenance cost. This reward function incorporates the maintenance cost …