Production scheduling, maintenance planning, and process quality control are important components of manufacturing efficiency, although they are generally addressed independently. This study proposes an integrated optimization model that takes into account preventative maintenance scheduling, quality control measures, and production sequencing. By coordinating these factors, the proposed approach decreases overall operational costs, imroves process reliability, and reduces scheduling interruptions. The model optimizes preventive maintenance intervals and statistical process control (SPC) chart parameters in order to produce cost-effective and efficient manufacturing results. A numerical case study shows that combining maintenance and quality control reduces quality-related costs by 22% and scheduling disruptions by 18% when compared to independent strategies. The findings emphasize the significance of holistic decision-making in manufacturing systems. Furthermore, future research directions include the use of machine learning and real-time IoT data to improve predictive maintenance and adaptive scheduling. The proposed methodology offers practical benefits for both industry and academia by improving system reliability, lowering costs, and increasing overall operational efficiency.