Owing to the importance of municipal waste as a determining factor in waste management, developing data-driven models in waste generation data is essential. In the current study, solid waste generation is taken as the function of several parameters, namely month, rainfall, maximum temperature, average temperature, population, household size, educated man, educated women, and income. Two different stand-alone computational models, namely, gene expression programming and optimally pruned extreme machine learning techniques, are used in this study to establish their reliability in municipal solid waste generation forecasting, followed by Mallow’s coefficient feature selection method. The lowest Mallow’s coefficient defines the optimal parameters in solid waste generation forecasting. The novel hybrid models of intrinsic time-scale decomposition-gene expression programming and intrinsic time-scale decomposition- optimally pruned extreme machine learning methods based on Monte-Carlo resampling are employed, and an empirical equation is presented for solid waste generation prediction. For examining the reliability of these models, five statistical criteria, namely coefficient of determination, root mean square error, percent mean absolute relative error, uncertainty at 95% and Willmott’s index of agreement, are implemented. Considering Willmott’s index, the Monte Carlo-intrinsic time-scale decomposition-gene expression programming model attains the closest value (0.957) to the ideal value in the training stage and 0.877 in the testing stage. The hybrid ensemble model of intrinsic time-Scale decomposition-gene expression programming presented lower values of root mean square error (12.279) and percent mean absolute relative error (4.310) in the training phase and in the testing, phase compared to gene expression programming with (12.194) and (5.195), respectively. Overall, the prediction results of the hybrid model of intrinsic time-scale decomposition-gene expression programming using Monte-Carlo resampling technique agrees well with the observed solid waste generation data.