Integrating maintenance and production decisions in advanced manufacturing industries is key to improving productivity, reducing costs, and increasing system reliability. Given that the production rate directly affects the equipment failure rate, ignoring this interaction can lead to increased operational costs, reduced product quality, and unplanned downtime. Therefore, designing intelligent decision-making policies that simultaneously adjust maintenance plans and production rates while considering equipment condition becomes important. In this paper, a dynamic decision-making model for the joint management of maintenance policies and production rates in a series production system with two identical components is presented. In this system, production is only possible when both components are in an operational state, and the failure or stoppage of either component for maintenance activities leads to a complete system shutdown. The model assumes that the system failure rate is directly related to the production rate, such that increasing production raises the expected failure rate. By utilizing condition monitoring of the components, at the beginning of each decision period, the system's condition is assessed, and one of two actions is taken: performing maintenance activities (preventive or corrective) or adjusting the production rate. The problem is formulated as a Markov decision process model and solved using the Q-learning algorithm. The objective of the proposed model is to minimize the total system costs over a finite planning horizon.