This repository details the process of record linkage utilizing machine learning techniques, starting with the generation of synthetic datasets. Initially, synthetic data is created to simulate realistic scenarios and assess linkage models in a controlled environment. Following the validation and refinement of these models, the techniques are deployed on real-world data, enhancing the robustness and accuracy of the record linkage process. This comprehensive approach allows for a thorough evaluation of methods before applying them to actual datasets, ensuring reliable and insightful outcomes in population health research.