A stockpile typically refers to a large, accumulated bulk of loose materials, such as sand, soil, gravel, asphalt, salt, coal, and waste. These stockpiles are maintained by various industries, including construction, mining, quarrying, agriculture, energy, and waste management. Accurate volume estimation of stockpiles is crucial in these industries for effective inventory management, reliable cost estimation, efficient resource planning, enhanced profitability, and reduced waste. However, an accurate, cost-effective, and efficient method for estimating the volume of stockpiles is limited. Although iPhone-based Light Detection and Ranging (LiDAR) has been used in stockpile volume estimation, it has not been tested on both regular and irregularly shaped stockpiles. Accordingly, this study aims to assess the capability of iPhone LiDAR for accurately estimating the volume of both regular and irregularly shaped stockpiles. Six physical models were scanned using the LiDAR sensor available on the iPhone 12 Pro Max mobile phone. iPhone LiDAR point cloud was processed, and volumes were calculated using the free open-source CloudCompare software. For the ground truth, dimensions of all regular-shaped physical models were measured and averaged to calculate the volume manually. The volume of the irregular-shaped sand pile was estimated using a measuring cup. The Mean Absolute Percentage Error (MAPE) of volume estimation of regular-shaped stockpiles ranged from 0.56 % to 1.88%. However, the MAPE of volume estimation of the irregularly shaped sand stockpile was 4.45%. The results indicated that iPhone LiDAR is particularly effective for estimating the volume of both regular and irregularly shaped stockpiles, offering highly accurate measurements in such cases.