Hair loss disorders such as Alopecia Areata pose significant diagnostic challenges, often requiring labor-intensive visual assessments or microscopic ex-amination. Such classical approaches are laborious and prone to subjectivity. In this paper, we propose HairLossMultinet, a hybrid deep learning framework that leverages the representational strengths of ResNet50 and VGG19 to improve the hair damage classification with automated approaches. Unlike earlier approaches that relied on single-model pipelines, our approach employs a multi-scale feature fusion scheme, combining high- and low-level visual patterns for better classification accuracy. We experimented on a public database containing 5,304 images labeled as ‘Damaged’ and ‘Normal’ hair conditions, which was not medically annotated. Expansive experimentation with eight state-of-the-art convolutional neural networks verifies the superior performance of the proposed model, which achieved 98% accuracy. In addition, Grad-CAM was employed to justify the model’s predictions and indicate the most significant region to its conclusions. While promising, restricted dataset diversity represents a limitation to clinical generalizability. This paper serves as a foundation for future research in explainable AI in dermatological diagnosis.