The invention discloses an engine emission real-time prediction method, which comprises the following steps of: firstly, acquiring a plurality of known engine emission historical test data samples, dividing the samples into a training set and a test set to train a neural network, and calculating neural network output root-mean-square errors under different hidden layer nodes to determine a neural network topological structure; and then the initial weight and threshold of the neural network are optimized through a mind evolutionary algorithm, and finally an engine emission real-time prediction system is established by using an Adaboost algorithm. The problems that an existing engine emission data acquisition mode wastes time and labor, is limited by environmental factors, is high in instrument cost, is poor in transient emission measurement performance and the like are solved, and the transient emission data of the engine can be measured only by simply measuring the rotating speed, torque, power, track pressure, air-fuel ratio, oil consumption, EGR (exhaust gas recirculation) rate and SOI (oil injection time) in the operation process of the engine. Therefore, transient NOx emission, THC emission and CO emission of the engine can be accurately predicted in real time.