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Abstract Neutrinoless double-beta decay ( $0\nu \beta \beta$) is a rare nuclear process that, if observed, will provide insight into the nature of neutrinos and help explain the matter-antimatter asymmetry in the Universe. The large enriched germanium experiment for neutrinoless double-beta decay (LEGEND) will operate in two phases to search for $0\nu \beta \beta$. The first (second) stage will employ 200 (1000) kg of High-Purity Germanium (HPGe) enriched in⁷⁶Ge to achieve a half-life sensitivity of 10²⁷(10²⁸) years. In this study, we present a semi-supervised data-driven approach to remove non-physical events captured by HPGe detectors powered by a novel artificial intelligence model. We utilize affinity propagation to cluster waveform signals based on their shape and a support vector machine to classify them into different categories. We train, optimize, and test our model on data taken from a natural abundance HPGe detector installed in the Full Chain Test experimental stand at the University of North Carolina at Chapel Hill. We demonstrate that our model yields a maximum sacrifice of physics events of ${0.024}_{-0.003}^{+0.004}\mathrm{\%}$after data cleaning. Our model is being used to accelerate data cleaning development for LEGEND-200 and will serve to improve data cleaning procedures for LEGEND-1000.