Multispectral LiDAR (MSL) systems offer a significant advantage by actively capturing both spatial and spectral information. These systems offer significant promise in supporting the comprehensive analysis and precise classification of underwater targets. In this study, we build an MSL system based on an acousto-optic tunable filter (AOTF) to investigate the feasibility of underwater sphere classification. The MSL prototype features a spectral resolution of 20 nm and 13 spectral channels, covering a range from 560 to 800 nm. Laboratory-based experiments were conducted to evaluate the accuracy of range measurements and the classification performance of the system. The spectral curves of nine distinct spheres acquired by the MSL were utilized for classification using a support vector machine (SVM). The experimental results indicate that classification using multispectral data yields a higher accuracy and Kappa coefficient. Finally, the point cloud acquired from scanning experiments further validated the MSL system’s performance. This finding preliminarily validates the feasibility of multispectral LiDAR for classifying submerged spherical targets.