Algebraic multigrid (AMG) is widely used to accelerate largescale sparse linear solvers.In distributed environments, neighboring communication overhead in AMG significantly impacts overall solution time.We propose Communication-Reduced Algebraic Multigrid (CRAMG) methods to minimize inter-process data exchange and message count by fusing interpolation/restriction operators with residual computations.This reduces communication frequency from four per level to as few as two.Experiments show up to 45% reduction in data exchange and 35% fewer messages.Performance evaluations on an Intel platform demonstrate significant improvements