Laryngeal cancer has been identified as a very critical disease in the modern era. Laryngeal cancer identification in the beginning stage is one of the most challenging tasks for clinicians which may hinder the patient’s diagnosis in the initial stage. In recent years, multifarious computer-aided (CAD) screening approaches have been suggested by investigators for the timely prognosis of laryngeal cancer patients. Nevertheless, these developed screening instruments still have multiple limits namely low accuracy in laryngeal cancer analysis, high computing cost, intricacy in generalization, time complexity, and many more. In this research, an enhanced mobile application-based deep learning (DL) hybrid framework is proposed for laryngeal cancer analysis using voice and image data. The key aim of the mobile application-based DL framework is to determine the laryngeal cancer existence in the beginning phase with high accuracy for effective diagnosis of laryngeal cancer patients. Our proposed DL framework is validated through multiple image and voice benchmark datasets namely ImageNet, CT imaging dataset, Massachusetts Eye and Ear Infirmary (MEEI), and Saarbrucken Voice Dataset (SVD). The observed performance of our mobile application-based deep learning framework is very optimal and improved for laryngeal cancer patient screening. The evaluation metrics namely accuracy, precision, recall, and F1-score for the suggested laryngeal cancer detection model are observed at 99.29%, 98.76%, 98.39%, and 98.26%, respectively. There is promising scope for additional investigations in the future to explore novel methodologies based on DL applications for laryngeal cancer identification effectively, using extensive voice and image datasets.