Cell tracking in chimeric models is essential yet challenging, particularly in developmental biology, regenerative medicine, and transplantation studies. Existing methods, such as fluorescent labeling and genetic barcoding, are technically demanding, costly, and often impractical for dynamic, heterogeneous tissues. To address these limitations, we propose a computational framework that leverages sex as a surrogate marker for cell tracking. Our approach uses a machine learning model trained on single-cell transcriptomic data to predict cell sex with high accuracy, enabling clear distinction between donor (male) and recipient (female) cells in sex-mismatched chimeric models. The model identifies specific genes critical for sex prediction and has been validated using public datasets and experimental flow sorting, confirming the biological relevance of the identified cell populations. Applied to skeletal muscle macrophages, our method revealed distinct transcriptional profiles associated with cellular origins. This pipeline offers a robust, cost-effective solution for cell tracking in chimeric models, advancing research in regenerative medicine and immunology by providing precise insights into cellular origins and therapeutic outcomes.