With the increasing intelligence, informatization, and systematization of modern warfare, future operations demand real-time combat effectiveness evaluation and efficient decision-making. To address the effectiveness evaluation problem in Manned Aerial Vehicle (MAV)/Unmanned Aerial Vehicle (UAV) cooperative air-to-ground combat, an intelligent assessment method based on combat simulation deduction and artificial neural networks is proposed. Supported by the simulation deduction system, evaluation data are obtained through constructing a combat effectiveness evaluation index system, designing simulation deduction processes, and synthesizing evaluation results. BP neural network is employed to train the data and verify the training effectiveness. Case analysis is used to validate the feasibility of the method, while sensitivity analysis investigates key indicators of various schemes and their impacts. The proposed method provides technical references for effectiveness evaluation of MAV/UAV cooperative combat, equipment improvement research, and rapid operational decision-making.