Accurate prediction of solid waste (SW) generation is considered an important aspect of waste management. It plays a major role in strategy development especially in developing countries. In this work, six independent variables related to the state of Kuwait were used as inputs for the development of multivariable regression models. The aim was to predict SW generation rates from a number of sectors within the country, namely the domestic, commercial, building and construction (B&C), and agricultural ones. The variables included comprised the total population of the country, gross domestic product (GDP) index, construction area, cost of utilised constructed agricultural area and total agricultural production requirements. Statistical analysis was used to confirm the reliability of the regression models developed. The results indicated that predications were highly accurate with standard errors (SE) ranging between 3.52% and 10.46% for the indicators of the multiple regression predictive models. Multiple-variable regression models developed showed mean standard errors ranging between 0.125 and 1.09% for the dependent variables considered. The developed regression models can be used to predict individual SW components which could be used by decision makers when devising measures and policies for long-term SW management strategies. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.