To address issues such as leaf occlusion, misalignment of the harvesting robotic arm, and limited harvesting range in hillside chili fields, this paper designs an intelligent harvesting system based on 3D point cloud reconstruction and multi-mechanism collaborative leveling. The system integrates real-time data from a LiDAR and IMU inertial navigation system to reconstruct the chili point cloud occluded by leaves from multiple perspectives. To address issues such as misalignment of the robotic arm caused by terrain undulations, the system integrates an adaptive leveling platform and an H-shaped planar slide, combined with a gyroscope to dynamically adjust the arm’s posture in real time, ensuring arm stability while expanding its workspace. In addition, to ensure harvesting efficiency and pepper integrity, an integrated cutting–gripping flexible end effector is designed to achieve synchronized cutting and collection operations. The experiment shows that the system achieves recognition accuracy of 81.95% for occluded chili peppers and 89.04% for non-occluded chili peppers. The harvesting success rate is 86.33%, with a single harvesting operation taking 13.17 s. During prolonged operation, the harvesting success rate can be maintained at approximately 85.1%. In summary, the intelligent harvesting system based on 3D point cloud reconstruction and multi-mechanism collaborative leveling provides a feasible solution for automated pepper harvesting.