With the continuous growth of urban governance targets, data scale, and operational constraints, traditional informatization models are no longer sufficient to meet the requirements of smart cities for real-time perception, collaborative decision-making, and refined governance. This paper systematically reviews the current application status, existing obstacles, and optimization paths of artificial intelligence (AI) in smart city construction, focusing on three high-frequency scenarios: traffic management, urban energy, and public safety. Based on English literature from the past three years, international city rankings, and relevant OECD statistics, an analytical framework of "data-algorithm-scenario-governance" is constructed. The study argues that AI has gradually shifted from single-point identification and local prediction to cross-departmental collaboration, digital twin simulation, and human-centered governance optimization; however, significant shortcomings remain in areas such as data silos, algorithm reliability, institutional coordination, cost constraints, and citizen participation. Therefore, this paper proposes a layered data governance model, scenario-based model deployment, a closed-loop reliability assessment system, and a multi-stakeholder co-governance mechanism to improve the efficiency, resilience, and sustainability of smart city construction.

Jingyi Zeng. Research on the Application of Artificial Intelligence Technology in Smart City Construction—An Analysis Based on Collaborative Scenarios of Traffic Management, Urban Energy, and Public Safety. Machine Learning Theory and Practice (2026), Vol. 6, Issue 1: 59-67. https://doi.org/10.38007/ML.2026.060107.

[1] Wu, L. (2025, December). Design and Application of Automatic Data Set Generation Tool Based on KLEE in Embedded Memory Management Performance Test Framework. In 2025 IEEE 17th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 1111-1117). IEEE.

[2] Huang, J. (2025, August). Research on Multi-Model Fusion Machine Learning Demand Intelligent Forecasting System in Cloud Computing Environment. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-7). IEEE.

[3] Huang, J. (2025, September). Performance Evaluation Index System and Engineering Best Practice of Production-Level Time Series Machine Learning System. In 2025 International Conference on Intelligent Communication Networks and Computational Techniques (ICICNCT) (pp. 01-07). IEEE.

[4] Sun, Q. (2026). Research on Lightweight Intelligent Dialogue Systems Based on Semantic Entity Enhanced Intention Recognition and Rule Retrieval Generation Hybrid Models.

[5] Wang, Y. (2026). Research on the Application of Artificial Intelligence in Supply Chain Risk Early Warning.

[6] Sun, Q. (2026). Research on a Robotic Natural Language Intelligent Decision-Making Framework Based on Large Language Models, Thinking Chain Reasoning, and Multi-Agent Collaboration.

[7] Liu, H. (2026). Research on the Application of Causal Reasoning Method in Content Compliance Experimental Evaluation.

[8] Yu, X. (2025). Digital Transformation Empowers Growth Marketing with Marketing Data Analysis Integration and Real-Time Display Strategy.

[9] Yu, X. (2026). Strategy Models and Practical Research of Growth Marketing under the Background of Digital Transformation.

[10] Yin, J. (2026). Research on Financial Time Series Prediction and Multiscale Correlation Based on the Fusion of Network Big Data and Deep Learning.

[11] Hou, Y. (2026). Research on BIOS and BMC Compatibility Optimization Methods for Cross-Generation Servers in Production Environments.

[12] Han, X. (2026). Research on Process Decision-Making Behavior under Incomplete Information Conditions in Automobile Manufacturing Systems.

[13] Liu, H. (2026). Research on Dynamic Price Prediction of E-commerce Based on Time Series Modeling.

[14] Han, X. (2026). Research on Automotive Manufacturing Process Optimization Methods for Multi-Supplier Collaboration.

[15] Zheng, H. (2026). Research on Edge Computing Network Task Scheduling and Resource Management Optimization Based on Artificial Intelligence Technology.

[16] Zheng, H. (2026). Research on Edge Computing Deep Neural Network Task Unloading Based on Resource Collaboration Framework and Multi Strategy Optimization.

[17] Zhixian Zhang. Research on Model Engineering Integration Methods for AI Systems Based on Data-Driven Intelligence. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 140-149.

[18] Ma, X. (2026). Research on End-To-End Reliability Modeling and Optimization of Service Grid.

[19] Zinuo Wang. Value Reassessment Logic of Resource-Based Enterprises in the Context of Energy Transition. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 141-149.

[20] Weiyao Ma. Automated Operation Approach for Scalable Cloud Data Platform. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 131-139.

[21] Zelin Wang. Data Analysis and Risk in Supply Chain Management. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 132-140.

[22] Ye, J. (2025). Design of a Non-Invasive Brain Computer Interface System for Handwritten Text Based on L2 Regularization and Attention Supervision Paradigm, and Optimization of EEG Signal Decoding. International Journal of Big Data Intelligent Technology, 2025, 6 (1), 126, 134.

[23] Yin, J. (2026). Research on a CLO Secondary Market Spread Volatility Prediction Model Based on RoBERTa Sentiment Factors. Advances in Computer and Communication, 7(1).

[24] Liu, H. (2025). Research on the Evaluation of User Safety Intervention Measures Based on Causal Inference. Engineering Advances, 5(4).

[25] Wang, B. (2025). Methods of Load Optimization for Computer Systems Based on Physical Principles.

[26] Wang, B. (2025). Application of Efficient Load Test Strategies in Infrastructure. Journal of Computer, Signal, and System Research, 2(4), 69-75.