The pharynx plays a most significant role in serving both the body’s air food passageway systems. Pharyngeal neoplasm happens when tissues in the throat grow and spread abnormally, destroying healthy cells. So, pharyngeal neoplasm detection plays a vital role in timely and accurate diagnostic methods for improved patient result outcomes. In this project, we propose a machine learning based classification system for the automated detection of pharyngeal neoplasm using medical imaging data. A convolution neural network (CNN) architecture was designed and trained on the labelled dataset for classification. The CNN model demonstrates remarkable efficiency in the difference between normal pharynx anatomy and multiple neoplasm symptom performance evaluation metrics, including accuracy, sensitivity, specificity, and area under the receiver operating characteristics (ROC) curve, which was utilized to access the model’s diagnostic capability. So, our motto is to provide promising accuracy in pharyngeal neoplasm detection. The pharynx serves as a crucial intersection for both the respiratory and digestive systems, making its health paramount. Pharyngeal neoplasms, characterized by abnormal tissue growth, pose a significant threat, necessitating timely and accurate detection for optimal patient outcomes. In this study, we propose a novel approach utilizing machine learning for automated pharyngeal neoplasm detection through medical imaging analysis. A convolutional neural network (CNN) architecture was meticulously designed and trained on a meticulously curated dataset, annotated by expert clinicians. The CNN model exhibits exceptional performance in discerning between normal pharyngeal anatomy and various neoplasm presentations. Performance metrics including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve were employed to evaluate the model’s diagnostic prowess. Our results underscore the promising accuracy and efficacy of the CNN based classification system in pharyngeal neoplasm detection. By harnessing the power of machine learning, we aim to revolutionize diagnostic methods, enabling early intervention and improved patient outcomes in the realm of pharyngeal pathology.