Detecting the Gingival Phenotype (GP) is a crucial component in dental treatment planning, as it helps evaluate the thickness and quality of gingival tissues. Conventional methods, such as direct assessments and probe transparency, are commonly employed in preventive dentistry. However, these methods are invasive and can cause discomfort. This research examines the application of deep learning as a non-invasive alternative for identifying the Gingival Phenotype from intraoral images, offering a more patient-friendly approach in the dental field. A dataset comprising 412 intraoral images of gingiva was gathered from Ranjeet Deshmukh College of Dental Sciences and Research. The dataset was augmented to increase the total number of images to 1,236. Three deep learning models—Sequential CNN, VGG-16, and ResNet50—were trained using transfer learning techniques on this gingival phenotype dataset. The Sequential CNN, VGG-16, and ResNet50 models achieved accuracy rates of 64.51%, 89.51%, and 93.54%, respectively. Given these outcomes, ResNet50 was identified as the most effective model for detecting Gingival Phenotype and was chosen for implementation in a web-based application for real-time usage. This web-based application serves as a non-invasive, highly accurate tool for oral health professionals, allowing for more precise diagnosis and treatment of gingival diseases This application could significantly improve the diagnosis and management of gingival disorders, leading to better patient outcome.