As a fast-growing and multifunctional crop, bamboo plays a pivotal role in food security and climate change mitigation by leveraging its high carbon sequestration potential. Monitoring aboveground carbon (AGC) stock in bamboo forests is crucial for guiding field management, growth observation, and yield prediction. Unmanned aerial vehicle (UAV)-based point cloud sensors offer a rapid and scalable solution for measuring bamboo AGC. This study evaluates the potential of UAV-LiDAR and machine learning (ML) for organ-level AGC estimation in bamboo forests. From LiDAR point clouds, we extracted structural features—including height, density, canopy, and intensity metrics—aggregated by mean plot-level metric (Mean-PM) and maximum plot-level metric (Max-PM) values at a 1 m² grid scale. Key predictors were selected using ML-based recursive feature elimination (ML-RFE) to develop organ-specific AGC inversion models. Results showed that organ-specific carbon content and allometric equations effectively eliminated biases associated with a uniform coefficient. Max-PM features outperformed Mean-PM features in stem and leaf AGCs, with the XGBoost and Random Forest models achieving the highest accuracy (R² = 0.82 for stems, 0.73 for leaves). Height percentiles and canopy structural metrics emerged as dominant predictors. This UAV-LiDAR-ML framework provides a cost-effective solution for precise bamboo carbon estimation, offering critical insights for carbon neutrality management and informed decision-making in bamboo forest ecosystems.