Artificial intelligence (AI) has considerably advanced the methods for individualizing language learning opportunities, such as assessing learning progress and recommending effective individual instruction. In the present study, we conducted a meta-analysis to synthesize recent empirical findings pertaining to the utilization of AI-guided language learning and collected 61 samples (N = 8,282) from 17 research projects (e.g., Assessment to Instruction [A2i], Duolingo, and Project LISTEN). The results of our meta-analysis confirmed that AI-guided individualized language learning was effective for learners' language development (d = 1.18, based on 26 within-group samples, N = 2,262) and had an overall positive treatment effect compared to business-as-usual conditions (d = 0.39, based on 35 between-group samples, N = 6,020). Moreover, the results of our moderator analyses for the treatment effect revealed that AI-guided language learning with machine learning and hybrid systems were more impactful than those with rule-based systems, which may be more helpful (compared to the former) in understanding how predictions are made from a pedagogical perspective. Evidence-based implications are provided based on the results of this meta-analysis.