This study explores how the integration of predictive models with machine learning and natural language processing can optimise community-based service operations, using Timebanking UK as a case study. The research evaluated these models in terms of their effectiveness in enhancing service matching, automating text classification, and improving interaction quality. Additionally, the study addressed privacy concerns using synthetic data generation. The findings indicate that data-driven approaches can streamline service delivery, mitigate social isolation, and foster community engagement. This provides a framework for the broader application of predictive models within community and health systems.