Sustainable development requires multidimensional assessment beyond GDP, as nations similar in economic performance often diverge in environmental resilience, social equity, financial robustness, and demographic conditions. This study utilizes advanced machine learning methods on the Harmonic Development Index (H2DI), an integrative composite indicator covering economic, financial, environmental, social, demographic, and knowledge-based dimensions. Employing a Self-Organizing Map (SOM), we identify topology-preserving clusters, visualizing nuanced country proximities and sustainability trade-offs beyond traditional linear models. Complementarily, a Bayesian network uncovers conditional dependencies among sustainability pillars, highlighting critical pathways influencing national development trajectories. Our approach addresses common limitations of PCA and k-means methods by capturing nonlinearities and providing probabilistic insights into sustainability dynamics. Results reveal consistent patterns, robust economic and financial sustainability correlate positively with social resilience and knowledge capacity but inversely with demographic vitality. Temporal robustness checks from 2005 to 2023 affirm stability of these relationships despite global shocks, validating the framework’s applicability for sustainable policy guidance.