<p>Facial kinship verification (FKV) is an automated process that determines whether individuals are biologically related based on facial features. It poses significant challenges in image processing and pattern recognition due to subtle inter-family variations. FKV has practical applications in missing person searches, image annotation, and child adoption. Although much work has been done on kinship verification, many challenges remain. Early approaches relied on handcrafted features and metric learning techniques. Recently, deep learning (DL) models–especially convolutional neural networks (CNNs)–have shown improved performance in this domain. This survey presents a comprehensive review of DL-based methods for kinship verification with critical analysis. It also includes an overview of traditional handcrafted and metric learning approaches, a comparative analysis of existing DL models on various kin-pair datasets, and a detailed examination of publicly available FKV datasets. Key challenges and open research gaps are identified to guide future developments in the field.</p>