•Models for predicting municipal solid waste (MSW) were developed.•Municipal level prediction was performed.•Socio-economic and demographic parameters were used.•Application of neural networks and decision tree algorithms were successful.•MSW generation was predicted with 16–23% prediction error. The main objective of this study was to develop models for accurate prediction of municipal solid waste (MSW) generation and diversion based on demographic and socio-economic variables, with planned application of generating Canada-wide MSW inventories. Models were generated by mapping residential MSW quantities with socio-economic and demographic parameters of 220 municipalities in the province of Ontario, Canada. Two machine learning algorithms, namely decision trees and neural networks, were applied to build the models. Socio-economic variables were derived from Canadian Census data at regional and municipal levels. A data pre-processing and integration framework was developed in Matlab® computing software to generate datasets with sufficient data quantity and quality for modeling. Results showed that machine learning algorithms can be successfully used to generate waste models with good prediction performance. Neural network models had the best performance, describing 72% of variation in the data. The approach proposed in this study demonstrates the feasibility of creating tools that helps in regional waste planning by means of sourcing, pre-processing, integrating and modeling of publically available data from various sources.