Global smart cities classification using a machine learning approach to evaluating livability, technology, and sustainability performance across key urban indices

Smart cities have become an increasingly important response to urbanization challenges, integrating technology to enhance city infrastructure, services, and sustainability. This study aims to classify the highest 50 global smart cities based on key livability and technology indices, using advanced machine learning (ML) models to assess city performance comprehensively. The necessity of this research lies in its focus on identifying patterns and best practices among high-performing cities, offering actionable insights for urban planners and policymakers aiming to improve smart city initiatives. This approach is necessary for understanding and replicating best practices in urban management and smart city development. Focusing on high-ranking cities ensures the study analyzes robust and reliable data, avoiding noise and inconsistencies arising from lower-performing or less-documented cases. Drawing on data from the Smart Cities Index (SCI) and other economic and sustainability competitiveness metrics, the study uses various ML algorithms to categorize cities into performance classes, ranging from high-achieving Class 1 to emerging Class 3 cities. The methodology involves data preparation with imputation and normalization, followed by training 9 supervised ML models. The results show that Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree are identified as the most effective classifiers. Furthermore, the results indicate that cities with well-integrated governance, infrastructure, and sustainability practices consistently rank higher, while cities in the lower classes face challenges in these areas. Policy implications suggest that cities aiming to enhance their smart city performance should prioritize comprehensive urban management strategies that balance technological infrastructure with sustainability and public service accessibility to drive more equitable and resilient urban growth.