Engineering management plays a crucial role in the successful execution of engineering projects by coordinating and controlling resources, planning, and ensuring timely delivery. However, with the increasing complexity of projects and the need for innovation and competitiveness, there is a growing demand for advanced methods to evaluate and analyze project performance. This paper proposes a novel approach using the particle swarm optimization backpropagation neural network for performance analysis in engineering management. In the first place, the improved particle swarm optimization (IPSO) algorithm that this study develops is an improvement on PSO with respect to both inertia weight and learning factor. Second, this paper employs IPSO technique to improve BP network to address the issue of poor convergence performance due to random initialization of network parameters. In conclusion, this work conducted a number of experiments on IPSO-BP, and the results of those experiments confirmed the method’s superiority. The integration of PSO and BP algorithms offers a promising solution to overcome the limitations of conventional methods. The findings of this research have practical implications for project managers, enabling them to make informed decisions and optimize project performance.