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  <doi_batch_id>aspg-41-4413-1792222617</doi_batch_id>
  <timestamp>20261017073657</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>Metaheuristic Optimization Review</full_title>
    <abbrev_title>MOR</abbrev_title>
    <issn media_type="electronic">3066-280X</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2026</year>
    </publication_date>
    <journal_volume>
     <volume>6</volume>
    </journal_volume>
    <issue>2</issue>
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   <journal_article publication_type="full_text">
    <titles>
     <title>AI-Driven Smart Cities: A Comprehensive Review of Technologies, Applications, and Future Directions</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Ahmed</given_name>
      <surname>Zakaria</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Artificial intelligence-based monitoring has become an essential technological direction in the development of smart cities, where large-scale sensing, data analytics, and automated decision-support systems are increasingly used to improve urban efficiency, sustainability, safety, and quality of life. As modern cities face growing challenges related to traffic congestion, environmental pollution, energy consumption, public safety, waste management, infrastructure degradation, and rapid population growth, conventional monitoring approaches are no longer sufficient for supporting timely and adaptive urban decision-making. This review examines the role of artificial intelligence in smartcity monitoring by analyzing how machine learning, deep learning, computer vision, Internet of Things sensing, edge computing, and cloud-based analytics contribute to real-time observation, prediction, anomaly detection, and intelligent control across different urban domains. The review highlights major application areas, including traffic-flow monitoring, air-quality prediction, energy management, smart surveillance, waste monitoring, disaster detection, infrastructure inspection, and public-service optimization. It also discusses how artificial intelligence enables cities to move from reactive management toward predictive and preventive governance by identifying hidden patterns in heterogeneous urban data and supporting faster responses to emerging risks. Despite these advantages, the deployment of AI-based monitoring in smart cities remains associated with several challenges, including data privacy, cybersecurity, algorithmic bias, limited interoperability, high infrastructure cost, dependence on reliable sensor networks, and the need for transparent and explainable decision-making. Overall, this review shows that AI-based monitoring can significantly strengthen the operational intelligence of smart cities when it is implemented within ethical, secure, scalable, and citizen-centered governance frameworks.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2026</year>
    </publication_date>
    <pages>
     <first_page>01</first_page>
     <last_page>13</last_page>
    </pages>
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     <item_number item_number_type="article-number">4413</item_number>
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     <doi>10.54216/MOR.060201</doi>
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