Accurate structural assessment and biomass quantification are critical for sustainable management and climate change mitigation in forest-based agroforestry systems. Traditional field-based inventory methods are labor-intensive, time-consuming, and often inadequate for capturing complex three-dimensional canopy architectures characteristic of multi-layered agroforestry landscapes. Light Detection and Ranging (LiDAR) technology has emerged as a transformative remote sensing tool enabling high-resolution three-dimensional modeling of vegetation structure at multiple spatial scales. This review examines the application of terrestrial, airborne, and unmanned aerial vehicle-based LiDAR systems for structural characterization and biomass estimation in agroforestry contexts. Key methodological approaches including point cloud processing, canopy height model generation, individual tree segmentation, and allometric model integration are critically evaluated. LiDAR-derived metrics demonstrate strong correlations with field-measured biomass and enable spatially explicit carbon stock assessment across heterogeneous agroforestry landscapes. Applications extend to productivity monitoring, land-use optimization, and climate-smart management strategies. Despite challenges related to data acquisition costs, processing complexity, and accuracy in dense understory conditions, LiDAR-based modeling represents a paradigm shift toward precision agroforestry management, facilitating evidence-based decision-making for resource-efficient and environmentally sustainable production systems.