Diagnosing Oral Cavity Cancer (OCC) in its initial stages is an effective way to reduce patient mortality. However, current pre-screening solutions are manual and the resultant clinical treatment isn’t cost-effective for the average individual, primarily in developing nations. We present an automated and inexpensive pre-screening solution utilizing Artificial Intelligence (AI) deployed on embedded edge devices to detect benign and pre-malignant superficial oral tongue lesions. The proposed machine vision solution utilizes a clinically annotated photographic dataset of 9 types of superficial oral tongue lesions to retrain a MobileNetV2 neural network using transfer learning. In this approach, we also utilized TensorFlow Lite for Microcontrollers to quantize a 32-bit floating-point (float32) precision model into an 8-bit integer (int8) model for deployment on power and resource-constrained OpenMV Cam H7 Plus embedded edge device.