With smart cities’ rapid development, Internet of Things (IoT) technology applications have become widespread. However, significant data gaps in smart city IoT systems often lead to inaccurate analytics and decision-making. This study proposes a novel imputation model using an improved Mahalanobis distance algorithm. The research first examines this improved algorithm, and then analyzes its performance in filling smart city IoT missing data. Results show that at a 10% missing rate, the proposed model achieves the lowest misjudgment rate (0.45), outperforming ARBI (0.90) and RFI (0.95) models. It also demonstrates the lowest error rate at 5% (0.32) and maintains superior performance at 30% missing data (error rate: 0.48). Compared to similar models, this approach shows highest accuracy in missing data imputation, proving its effectiveness and reliability for smart city IoT applications.
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