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The European-wide environmental obstacles of inefficient and unsustainable recycling systems and flows constrain household waste (HW) management, endangering the circular economy. The European 2020 strategy and ongoing environmental disasters indicate the ineffectiveness of the current HW sustainability practices. This paper introduces an artificial intelligence (AI) approach for calculating urban residual waste, based on its generation level. It reforms the current diverse and high discrepancy levels of HW residual for EU-countries and Ukraine. Adopting a k-means clustering method with a multi-criteria taxonomic development level index (TIDL), it produces uniform clusters with higher accuracy and manageability. Findings discover and remedy opaque managerial practices, enabling sustainable and environment-friendly development at national and regional levels for EU-countries. Results reveal an increased number of clusters in crisis, contributing to a methodological reference for environmental planning. In conclusion, this AI approach could have a European-wide impact on sustainable economic value-chain, converging toward an eco-friendly economy. [Display omitted] • Analysis of household waste management strategies in EU and Ukraine. • A methodological reference for environmental planning that moves away from the current centralized model. • Implementation of k-means household waste clustering approach with situational analysis and planning. • AI clustering approach with a multi-criteria taxonomic index for higher accuracy and manageability of waste. • Exploration of integrated AI-development economics for EU-Ukraine environmental policy. [ABSTRACT FROM AUTHOR]