Accurate estimation of body size is critical for understanding population structure and dynamics of whale sharks (Rhincodon typus), the world’s largest fish and a globally endangered species. However, global photo-identification (photo-ID) databases that often underpin population studies typically lack accurate information on body size, limiting their value for assessing demographic trends. This study investigates whether allometric relationships among body parts can be used to predict fork length (FL) in whale sharks, providing a foundation for estimating body size from two-dimensional (2D) photo-ID images. Diver-operated stereo-video (stereo-DOV) footage of 80 whale sharks was collected from an aggregation site at Ningaloo Reef in Exmouth, Western Australia, during the 2024 and 2025 seasons. Linear regression and Generalised Additive Models (GAMs) were used to evaluate the predictive capacity of morphometric measurements and ratios for estimating fork length in whale sharks. The top model incorporated morphometric measurements across the whale shark’s entire body, including the head, caudal fin, and body depths along the shark. Therefore, full body images of whale sharks would be required to accurately estimate FL from photo-ID databases. Overall, the results indicate the presence of allometric growth relationships in whale sharks and suggest that combinations of two morphometric ratios could effectively estimate fork length from 2D images. This approach offers a new method to enhance the accuracy of size data in existing whale shark photo-ID and stereo-video databases globally. Applying this method could improve assessments of population structure, growth rates, and long-term demographic trends, thereby strengthening global conservation and management efforts for this endangered species.