Background <p>Artificial intelligence is an increasingly valuable tool in ischemic stroke management. This systematic review and meta-analysis evaluated the effect of artificial intelligence implementation on stroke workflow metrics.</p> Methods <p>PubMed, EMBASE, OpenEvidence, and the Cochrane Central Register of Controlled Trials were searched for studies (2015−2025) evaluating automated large vessel occlusion detection. Pooled mean differences with 95% CIs were calculated for door-to-groin puncture, door-to-first pass, door-to-revascularization, door-to-needle, and door-in-door-out times.</p> Results <p>Twelve studies met the inclusion criteria: one clinical trial and eleven observational studies. Artificial intelligence was associated with reductions in multiple workflow intervals, including door-to-groin puncture (−17.12 minutes), door-to-first pass (−26.55 minutes), door-to-revascularizationon (−14.55 minutes), door-to-needle (−4.44&#xa0;minutes), and door-in-door-out time (−36.8 minutes).</p> Conclusion <p>Overall, AI-based platforms appear to contribute meaningfully to stroke workflow optimization, although randomized controlled trials are still needed to confirm their effectiveness.</p> Level of Evidence <p>Level 2c, Systematic review of randomized clinical trials, and observational studies.</p> Graphical Abstract <p></p>