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This study aims to use immune\u2010cell morphology to develop a deep\u2010learning model for this purpose. Overall, 1427 3D images of cerebrospinal fluid (CSF) immune cells from 14 patients with CNS infections are obtained using holotomography. The images are categorized into infection etiology groups (viral and non\u2010viral) and prognosis groups (based on the modified Rankin Scale score at discharge). A deep\u2010learning model is constructed to predict the etiology and prognosis of CNS infections using the immune\u2010cell morphology. Cell morphological features and spatial distribution of CSF immune cells differ significantly between patients in the viral and nonviral groups and between prognosis groups. The model yields areas under the receiver operating characteristic curve of 0.89 and 0.79 for the diagnosis and prognosis, respectively. As more cell images are used, the prediction and model robustness improve. With &lt;10 cells, both tasks exhibit a nearly 100% predictive performance. After dividing the cells into eight shells, significant refractive index variations are observed. This is the first study to use CSF cell morphology for the diagnosis and prognostication of CSF infections. 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