Accurate estimation of municipal solid waste (MSW) generation and identification of its key associative characteristics are important prerequisites for achieving refined global urban environmental management and differentiated governance. However, the scarcity of long-time-series data globally, especially in developing countries, and the insufficient capability of traditional models in resolving non-linear associative characteristics make it difficult for existing studies to accurately reconstruct the historical generation scale and evolutionary laws of global MSW. To this end, this paper established an intelligent estimation and cluster analysis framework for global MSW generation based on XGBoost and K-Means++ algorithms. The results indicate that XGBoost exhibits excellent generalization capability, with a coefficient of determination (R²) of 0.91±0.07 for MSW generation prediction in unknown countries; the comparison of cross-validation strategies confirms that the model successfully captures the intrinsic associations of socio-economic characteristics for MSW generation prediction, rather than merely relying on time trends, showing good spatial generalization reliability. SHAP interpretation analysis proves that population scale and economic volume are the core associative factors affecting MSW generation and reveals a unique "N-shaped" non-linear associative trajectory between GDP (in terms of purchasing power parity, PPP) per capita and generation, breaking the limitations of traditional linear extrapolation models. The estimation results indicate that the total global MSW generation has climbed from (1.29±0.04) billion t in 1991 to (2.14±0.16) billion t in 2021, with an average annual growth of 1.7%. Cluster analysis identifies four typical evolutionary patterns among global countries: agricultural subsistence, emerging industrial, mature stable, and high-consumption affluent patterns, among which emerging industrial countries are the main source of global MSW increment. The constructed prediction framework possesses good universal applicability and can provide a scientific basis for global solid waste′s refined management, regionally differentiated policy formulation, and infrastructure planning.