Abstract Background/objectives The large number and heterogeneity of causes of uveitis make the etiological diagnosis a complex task. The clinician must consider all the information concerning the ophthalmological and extra-ophthalmological features of the patient. Diagnostic machine learning algorithms have been developed and provide a correct diagnosis in one-half to three-quarters of cases. However, they are not integrated into daily clinical practice. The aim is to determine whether machine learning models can predict the etiological diagnosis of uveitis from clinical information. Methods This cohort study was performed on uveitis patients with unknown etiology at first consultation. One hundred nine variables, including demographic, ophthalmic, and clinical information, associated with complementary exams were analyzed. Twenty-five causes of uveitis were included. A neural network was developed to predict the etiological diagnosis of uveitis. The performance of the model was evaluated and compared to a gold standard: etiological diagnosis established by a consensus of two uveitis experts. Results A total of 375 patients were included in this analysis. Findings showed that the neural network type (Multilayer perceptron) (NN-MLP) presented the best prediction of the etiological diagnosis of uveitis. The NN-MLP’s most probable diagnosis matched the senior clinician diagnosis in 292 of 375 patients (77.8%, 95% CI: 77.4–78.0). It achieved 93% accuracy (95% CI: 92.8–93.1%) when considering the two most probable diagnoses. The NN-MLP performed well in diagnosing idiopathic uveitis (sensitivity of 81% and specificity of 82%). For more than three-quarters of etiologies, our NN-MLP demonstrated good diagnostic performance (sensitivity > 70% and specificity > 80%). Conclusion Study results suggest that developing models for accurately predicting the etiological diagnosis of uveitis with undetermined etiology based on clinical information is feasible. Such NN-MLP could be used for the etiological assessments of uveitis with unknown etiology.