<p>The pothole on roads is one of the major concerns of highway agencies across the world and the situation is even worse in India as potholes cause seven deaths a day on an average. Accurate data on the present condition of roads is necessary so that timely decisions can be made regarding when and where the repairs have to be carried out. The conventional method of manual road surveys is slowly being replaced by advanced technologies like Light Detection and Ranging (LiDAR) but the problem is the professional LiDAR instruments are highly expensive and may demand huge storage space and specialized skills to operate and use. In order to overcome these drawbacks, the present study proposed a polynomial regression based stack profile method for pothole measurement using Apple iPhone 12 Pro and 16 Pro Max LiDAR and it has been tested for 18 potholes in Vellore, India which are of varying size, shape and also at various stages of development. After removal of noises and plane fitting, the point cloud data of 18 potholes from Apple iPhone’s LiDAR were used to generate the Digital Elevation Model (DEM) and stack profiles in both longitudinal and transverse directions. Based on the Akaike Information Criterion (AIC), polynomial models of order 6 with R² of more than 0.8 were fitted on stack profiles passing through the potholes and using the developed models, geometric parameters of the potholes such as length, width, depth, area and volume were then extracted. The results revealed that the Apple iPhone’s LiDAR can be used to detect the potholes’ shape, its development stage and extract geometrical parameters such as length, width, depth, area and volume. The results obtained from Apple iPhone’s LiDAR are highly comparable with that of the professional terrestrial scanners like Leica BLK 360 with Mean Absolute Percentage Error (MAPE) of less than 10. It was also found that the proposed iPhone LiDAR based stack profile method is highly suitable to extract the geometric properties of other distresses such as ‘raveling’ as well. The present study also showed that the photogrammetry based Structure from Motion (SfM) technique lacks in point cloud density when compared to the iPhone LiDAR based approach. If highway agencies face budgetary constraints in purchasing highly expensive terrestrial laser scanners, then a viable low-cost alternative is to use the Apple iPhone’s LiDAR as demonstrated in the present study.</p>