Background/Objectives: Accurate identification of vulvar high-grade squamous intraepithelial lesions (HSIL) is essential for preventing progression to invasive squamous cell carcinoma. This study addresses the gap in artificial intelligence (AI) applications for vulvar lesion diagnosis by developing and validating the first convolutional neural network (CNN) model to automatically detect and classify HPV-related vulvar lesions—specifically HSIL and low-grade squamous intraepithelial lesions (LSIL)—based on vulvoscopy images. Methods: This bicentric study included data from 28 vulvoscopies, comprising a total of 9857 annotated frames, categorized using histopathological reports (HSIL or LSIL). The dataset was divided into training, validation, and testing sets for development and assessment of a YOLOv11-based object detection model. Results: The CNN demonstrated a recall (sensitivity) of 99.7% and a precision (positive predictive value) of 99.1% for lesion detection and classification. Conclusions: This is the first AI model developed for detecting and classifying HPV-related vulvar lesions. The integration of such models into vulvoscopy could significantly improve diagnostic accuracy and positively impact women’s health by reducing the need for potentially invasive and anatomy-altering procedures.