Data quality is essential for effective decision-making and evidence generation in health systems. Despite the increasing use of disease and health outcome registries, many systems suffer from missing, inconsistent, and inaccurate data, limiting their value for policy-making and service improvement. This study aims to develop and evaluate an AI (Artificial intelligence)-powered framework to enhance data quality in disease and health outcome registries by supporting key stages of data management, including data entry, evaluation, anomaly detection, and correction. This study adopts a mixed-methods design, conducted in five interlinked phases. Phase 1 includes systematic reviews of AI frameworks and data quality dimensions. In Phase 2, a set of data quality indicators will be identified and prioritized using focus groups and the Analytic Hierarchy Process (AHP). Phase 3 involves the design and Delphi-based validation of a comprehensive data quality management framework. Phase 4 will develop AI models to detect and address anomalies in the data. Phase 5 will test these models on a selected disease registry, assessing their effectiveness in improving data quality. Qualitative and quantitative data will be analyzed using thematic analysis,and statistical techniques. Ethical approval has been obtained from the Ethics Committee of Mashhad University of Medical Sciences (Approval Code: IR.MUMS.FHMPM.REC.1404.226). The study findings will be disseminated through peer-reviewed journals, conferences, and policy briefs. The results will also be presented in national and international forums, facilitating knowledge sharing and the potential adoption of the framework in other contexts.