Objectives: To review artificial intelligence (AI) algorithms for the assessment and classification of hearing loss (HL). Design: Systematic review Methods: Following PRISMA guidelines, five databases (MEDLINE, Embase, PubMed, Scopus, IEEE Xplore) were searched for studies using AI algorithms—including convolutional neural networks, artificial neural networks, support vector machines, and decision trees—to classify sensorineural, conductive, or noise-induced hearing loss. Results: Eighteen studies met inclusion criteria: six used binary classification, eight employed multi-class models, and four applied regression-based predictive modelling. Applications clustered into three domains: (1) pure tone audiometry (PTA), (2) imaging diagnostics (otoscopy, MRI), and (3) environmental exposure analysis. Multi-class PTA models achieved the highest performance (accuracy 96.8–99.8%, AUC >0.95). MRI-based algorithms averaged 96.6% accuracy, and otoscopic classifiers ranged from 81%–94.1% (mean AUC 0.963). Regression models predicted hearing thresholds with R² up to 0.92. Environmental noise exposure models reported 85–90% accuracy, with variability across approaches. Conclusion: AI demonstrates strong potential for rapid, accurate, and consistent hearing loss diagnosis. Heterogeneity exists in algorithm design and application. Future research should focus on multi-modal integration and prospective validation to ensure safe and effective clinical translation.