Application of AI and Machine Learning in Query Optimization: Evolutionary Trends, Bibliometric Insights, and Framework Analysis

Application of AI and Machine Learning in Query Optimization: Evolutionary Trends, Bibliometric Insights, and Framework Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Method Article Application of AI and Machine Learning in Query Optimization: Evolutionary Trends, Bibliometric Insights, and Framework Analysis Sadik Issah, Richard Amankwah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7318870/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract This paper provides a comprehensive review of the evolution of query optimization techniques, transitioning from traditional methods to Artificial Intelligence (AI) and Machine Learning (ML)-based approaches. The review bridges theoretical advancements with practical insights, offering researchers and practitioners a roadmap for adopting AI/ML-driven optimization in modern database systems. Employing a bibliometric analysis of Scopus database indexed publications from 2014 to 2024, the study maps research trends, key contributors, and thematic shifts in the field with the use of Biblioshiny and VOSViewer bibliometrics analysis tools. The study further systematically categorizes and evaluates state-of-the-art AI/ML frameworks focusing on their learning techniques, architectural models, and performance metrics. With China and the USA as the lead producers of articles relating to query optimization with AI/ML, the bibliometric analysis confirms rising interest and collaboration in this area. The annual scientific production shows an upward trend with 2024 being the highest record year with 2020–2022 year period serving as the transitional period the rise AI/ML query optimization related publications. artificial intelligence bibliometric analysis framework evaluation machine learning query evolution query optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers invited by journal 14 Sep, 2025 Editor assigned by journal 09 Sep, 2025 Submission checks completed at journal 08 Aug, 2025 First submitted to journal 07 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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