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Our taxonomic framework categorizes approaches across four dimensions: algorithmic methods, deployment environments, optimization objectives, and evaluation methods. Quantitative analysis demonstrates substantial AI superiority over traditional approaches:\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\approx $$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    45% average latency reduction (range 11\u221277.7%) from 10 studies with quantifiable latency data, 32% cost savings (range 10\u201348%) from 6 studies with quantifiable cost data, and 35% energy efficiency improvements (range 3.68\u201371%) from 16 studies with energy measurements. Reinforcement learning dominates the field (40% of studies) with particular effectiveness in dynamic environments, while hybrid approaches demonstrate superior multi-objective optimization. Critical research gaps include minimal carbon-aware scheduling integration (only 4 studies, 6.3% of corpus), over-reliance on simulation environments (70% of evaluations), and absence of standardized evaluation frameworks. The limited availability of quantifiable performance data across studies reveals a significant methodological gap in current research evaluation practices. We identify five high-priority research directions and provide actionable recommendations for advancing production-ready AI-driven cloud resource management systems that balance performance, sustainability, and practical deployment requirements.\n                  <\/jats:p>","DOI":"10.1007\/s00607-026-01655-8","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T06:55:08Z","timestamp":1776322508000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-driven resource allocation in cloud computing: a systematic review revealing critical sustainability and evaluation gaps"],"prefix":"10.1007","volume":"108","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6471-9263","authenticated-orcid":false,"given":"Shahzeb","family":"Alam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Atif","family":"Ramzan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7343-7101","authenticated-orcid":false,"given":"Muhammad","family":"Zubair","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ubaid","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Ziaullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7622-9632","authenticated-orcid":false,"given":"Rab","family":"Nawaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahmat","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"issue":"2","key":"1655_CR1","doi-asserted-by":"publisher","first-page":"1983","DOI":"10.1109\/TNSM.2023.3348124","volume":"21","author":"J Zhang","year":"2024","unstructured":"Zhang J, Yu H, Fan G, Li Z (2024) Elastic task offloading and resource allocation over hybrid cloud: a reinforcement learning approach. 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