Introduction Laryngeal disorders are a significant cause of voice-related morbidity, often requiring expert clinical judgment and invasive procedures for accurate diagnosis. The emergence of Artificial Intelligence (AI) offers a novel, non-invasive adjunct to traditional diagnostic methods. This experimental study evaluates the feasibility and accuracy of AI-based tools in diagnosing common laryngeal pathologies using voice signals and laryngoscopic imaging. Materials and Methods This experimental study was conducted in the ENT department of GSVM Medical College Kanpur. A total of 30 participants with voice change due to laryngeal pathology were recruited. Standard diagnostic workup including fiber optic laryngoscopy and voice assessment was performed. Simultaneously, laryngoscopic images and audio recordings were processed through a custom-designed AI model incorporating a convolutional neural network (CNN) for image classification and a machine learning-based classifier for acoustic signal analysis. Expert diagnosis served as the reference standard. Model performance was evaluated using metrics such as sensitivity, specificity, and accuracy. Results The AI model demonstrated high predictive performance, with a diagnostic accuracy of 91.3%, sensitivity of 89.7%, and specificity of 93.5% .The model was especially effective in identifying vocal cord nodules, polyps, and early malignant changes. Voice-based prediction algorithms showed good discrimination between functional and structural lesions. Conclusion This study demonstrates the potential of AI in enhancing the diagnostic accuracy of laryngeal disorders. AI tools, when integrated into clinical workflows, can serve as a rapid, cost-effective, and objective screening method—especially in settings with limited access to specialist care. Further large-scale studies and real-time clinical applications are warranted to validate these findings and expand the role of AI in otolaryngological diagnostics.