Abstract: Disclosed is an optimizing method for multi-source municipal solid waste combinations based on machine learning, including obtaining relevant property data, classifying the feature variables and obtaining a raw materials pre-combination from the classified feature variables according to a classification ratio, followed by cooperative combustion treatment to obtain data after combustion, summarizing the obtained data into a database, constructing a matrix of raw material components, operating conditions and pollutant distribution according to the database, obtaining matrix data; performing principal component analysis on the matrix data, constructing an information processing model, obtaining a data set of samples; carrying out training according to the data set to construct a relational model, obtaining processed parameters; training the obtained processed parameters to construct a regression module, an optimal parameter, and performing regression calculation using the optimal parameter together with the matr