Grasping target objects is a fundamental skill for robotic manipulation, but in cluttered environments with stacked or occluded objects, a single-step grasp is often insufficient. This paper proposes a geometry-aware push-grasp synergy framework that leverages point cloud data to integrate grasp and push evaluation. The grasp evaluation module analyzes the geometric relationship between the gripper's point cloud and the points enclosed within its closing region to determine grasp feasibility and stability. Guided by this analysis, the push evaluation module predicts how pushing actions influence future graspable space, enabling the robot to effectively manipulate objects in complex cluttered scenarios.