Smart meters (SM) generate critical data that provides real-time insights into energy consumption, grid performance, and load management, which are essential for improving grid reliability, energy efficiency, and renewable energy integration. However, achieving effective communication between smart meters and the control center remains a challenge due to limitations in Advanced Metering Infrastructure (AMI), including communication delays, metering technology constraints, and restricted data storage and processing capabilities. These limitations hinder the precision and timeliness of real-time data delivery, negatively impacting the efficiency of energy management and grid operations. While existing research predominantly focuses on optimizing communication network algorithms, the critical issue of comprehensive SM data scheduling has received limited attention. Moreover, current methods often fail to account for the complexity of communication networks and the dynamic nature of information flow. To address this gap, this paper introduces a novel method for scheduling SM data access by leveraging real-time data assessment and analysis. A quality metric termed mismatch probability evaluates data quality, and the Hungarian algorithm is employed to optimize meter scheduling. The proposed method is validated using real-world data from a Danish grid, demonstrating significant improvements in information quality for real-time monitoring compared to heuristic-based scheduling approaches.