Background and Objective Artificial intelligence-based recommender algorithms on social media platforms are specifically designed to maximise user engagement by systematically delivering large volumes of highly emotional content to adolescents, thereby creating a phenomenon known as algorithmic suicide risk. The present study aimed to systematically investigate the role of AI-driven recommender algorithms in social media in promoting suicide among vulnerable adolescents.Materials and Methods This systematic review examined 38 studies published between 2022 and 2025, retrieved from reputable scientific databases including PubMed, Scopus, Web of Science, Google Scholar, SID, and NoorMags. Findings Results demonstrated that simulated adolescent accounts were rapidly directed toward self-harm content within a short time and to explicit suicide-related content shortly thereafter. Prolonged and continuous exposure to algorithmically curated social media content exhibited a clear dose–response relationship, whereby greater duration and intensity of exposure were associated with statistically significant increases in the risk of suicidal ideation and suicide attempts. Sustained heavy exposure was associated with a more than twofold increased likelihood of suicidal ideation and attempted suicide compared with individuals with low consumption or no exposure to such algorithms. Key moderating factors included female gender, history of depression, and active searching for mental health-related content. Simple interventions, such as disabling personalised recommendation algorithms, reduced exposure to harmful content by up to 78%.Conclusion The current logic of these algorithms constitutes a modifiable causal factor in the ongoing epidemic of suicide among vulnerable adolescents, rendering immediate intervention essential.