Many people are affected by dental problems that have serious long-term effects. Thus, oral hygiene maintenance is necessary to prevent dry mouth tooth decay, bad breath, and cold sores, which adversely affect oral health. In this paper, we investigated patients’ oral health by applying optimized machine learning techniques to successfully identify potential pathologies present in the oral cavity. During the oral health analysis process, dental X-ray images are collected from a patient and examined by using the xeno-genetic spiking neural network. This method effectively examines tooth structure, gaps between teeth, and positioning of teeth such as molars, premolars, and incisors. The resulting information facilitates oral health maintenance. Finally, we evaluated the efficiency of the system with the help of computerized dental applications.